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Optimizing Resource Management in 5G Heterogeneous Edge Computing with Blockchain and Deep Learning 2

Optimizing Resource Management in 5G Heterogeneous Edge Comp...

引用

2nd IEEE international conference on Emerging Research in Computational Science, ICERCS 2024

作者： Mohammed, Nada Qasim Izzat Hassan, Sharmeen Manee, Mariam J. Alani, Hithaifa Hama Sharif, Shukur Ramadan Al-Nisour University College Baghdad Iraq Knowledge University College of Engineering Department of Computer Engineering Erbil Iraq Uruk University Department of Radiology Techniques Baghdad Iraq Al-Farahidi University Department of Medical Instruments Engineering Techniques Baghdad10011 Iraq Al Kitab University Department Medical Engineering Kirkuk Iraq

ISBN: (纸本)9798331534950

The introduction of smart edge computing (SEC), cloud-based apps might be installed on the network's edge, freeing up server resources for more computationally expensive services such as facial recognition and natural language processing. The user interface and device efficiency can be improved by offloading operations normally done by the device's core CPU to the edge. Better coverage for the SEC could benefit Internet of Things (IoT) apps that require a lot of resources. Consistent, steady, and rapid interaction between edge devices and central processing units is difficult to achieve, particularly in 5G Heterogeneous Network (HN) environments where access is dynamic and network devices have varied levels of available resources. As a result, this study presents a paradigm for intelligent resource management in smart edge computing (IMRSEC) that combines blockchain, edge computing, and AI with 5G HNs. A novel dual schedule deep reinforcement learning (DS-DRL) technique was developed, which combines quick and gradual learning schedules. Reduced emptying delay and increased system reserves are major priorities, which can be done by enhancing computation unloading, resource transit, and software caching deployment. Simulation results reveal that the proposed IMRSEC is a successful DS-DRL implementation, as well as a 32% reduction in job completion time using the suggested technique. © 2024 IEEE.

关键词： 5G mobile communication systems

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DQMix-BERT: Distillation-aware Quantization with Mixed Precision for BERT Compression

DQMix-BERT: Distillation-aware Quantization with Mixed Preci...

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2023 IEEE international conference on Systems, Man, and Cybernetics, SMC 2023

作者： Tan, Yan Jiang, Lei Chen, Peng Tong, Chaodong Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798350337020

Transformer-based architecture models like BERT have performed excellently for various natural language processing (NLP) tasks. However, these models are usually computationally expensive with a large number of parameters. As a result, deploying them in edge devices has become a challenging task. The existing compression work on lower-precision quantization still has a severe accuracy decrease and rarely focuses on the information hidden in the different modules of the model. In this paper, we propose a distillation-aware quantization with mixed precision method combined with quantization and knowledge distillation. We achieve the ultra-low mixed precision quantization with the different sensitivity of different modules of BERT. Moreover, we leverage knowledge distillation to reduce the model accuracy degradation. We extensively test our method on four GLUE tasks. It shows that DQMix-BERT outperforms the other BERT compression methods and even achieves comparable performance to the original BERT model while achieving 8x compression. © 2023 IEEE.

关键词： Bert compression knowledge distillation mixed precision quantization pre-training language model

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AyurChat: Empowering Health through AI-Driven Ayurvedic Guidance 15

AyurChat: Empowering Health through AI-Driven Ayurvedic Guid...

引用

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

作者： Ahmed, Syed Adeel Gahlawat, Gautam Imam, Fahad Saranya, G. College of Engineering and Technology S.R.M Institute of Science and Technology School of computing Department of Networking and Communications Tamil Nadu Kattankulathur603203 India

ISBN: (纸本)9798350370249

AyurChat is a project aiming to transform healthcare discussions by blending intelligence (AI) with the wisdom of Ayurveda. The innovative chatbot system, known as AyurChat acts as a health companion offering personalized advice, for medical queries. The main goal of AyurChat is to empower individuals to manage their health through natural solutions rooted in Ayurvedic principles. By utilizing cutting edge language processing and machine learning techniques, AyurChat engages in conversations with users understanding their health concerns and providing tailored guidance based on Ayurveda. This system aims to improve access to healthcare by bridging the gap between technology and traditional healing practices. With a ranging knowledge base covering Ayurvedic remedies AyurChat ensures thorough and precise responses to user's health inquiries. Through AI driven algorithms evolves over time, enhancing its grasp of user needs and refining its recommendations continuously. AyurChat stands as an endeavor that uses AI to make Ayurvedic healthcare knowledge more accessible. Offering discussions and personalized advice it provides users with treatments from the realm of Ayurveda rather than conventional Western medicine along with web links for alternative medicines, like Siddha and Unani. © 2024 IEEE.

关键词： Medicinal chemistry

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Recommendation Systems using Artificial Intelligence and using Machine Learning 15

Recommendation Systems using Artificial Intelligence and usi...

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15th international conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Turukmane, Anil V. Adhikari, Aviraj Das Chandini, Kollati School of Computer Science and Engineering VIT-AP University AP Vijaywada India

ISBN: (纸本)9798331300579

Recommender systems make individualized suggestions for users based on their actions and preferences by utilizing machine learning (ML) and artificial intelligence (AI).. These systems have evolved significantly, incorporating various AI techniques like fuzzy techniques, transfer learning, genetic algorithms, neural networks, deep learning, and more. The use of AI in recommender systems aims to enhance prediction accuracy and address data sparsity issues.1 Key methodologies in recommender systems include deep neural networks, transfer learning, active learning, fuzzy techniques, evolutionary algorithms, natural language processing, and computer vision.1 These techniques play crucial roles in knowledge representation, reasoning, planning, communication, perception, and image processing within recommender systems.1 Machine Learning plays a vital role in recommendation systems by utilizing algorithms like KNN clustering, Naive Bayes, collaborative filtering and content filtering to suggest products to users overwhelmed by information on e-commerce platforms.4 Additionally, Recommender Systems (RSs) are widely used across various domains such as e-commerce, tourism, health, and e-learning to enhance user experience and increase sales through personalized recommendations based on user preferences.5 RSs have become integral in guiding decisions for users in online transactions and improving the quality of their interactions with platforms like Amazon, Netflix, YouTube, Spotify, Facebook, and Twitter5 used to pinpoint people who are most at risk for developing complications from an illness or who are most likely to have poor treatment outcomes. These data can be used to develop personalized treatment plans for patients. © Grenze Scientific Society, 2024.

关键词： knowledge representation

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Querylizer: An Interactive Platform for Database Design and Text to SQL Conversion

Querylizer: An Interactive Platform for Database Design and ...

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2022 international conference for Advancement in Technology, ICONAT 2022

作者： Deshpande, Anushka Kothari, Dhruv Salvi, Akash Mane, Prajakta Kolhe, Vaishali D. Y. Patil College of Engineering Department of Computer Engineering Pune India

ISBN: (纸本)9781665425773

Every day, the world becomes more data-driven. Also, an enormous amount of data generated is managed by RDBMS in the form of Tables and SQL is the language that is used to manipulate it and perform the desired operations. Not all individuals have the required knowledge of SQL language to work on the databases. This paper proposes a system that will convert natural language statements into SQL queries. natural language processing, which is a component of AI, will be used by the system. NLP gives the ability to a computer program to understand human language in both spoken and written form. Moreover, the visualization of any system makes it easier to comprehend. Focusing on the Low-Code revolution presently in the industry, a visualization software for database management is proposed. © 2022 IEEE.

关键词： Visualization

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Emotional Polar Predictions Based on Matching Prompts 8

Emotional Polar Predictions Based on Matching Prompts

引用

8th international conference on Intelligent Computing and Signal processing, ICSP 2023

作者： Yang, Zhangping Ye, Xia Xu, Hantao Zhao, Pujie Rocket Army Engineering University Academy of Combat Support Xi'an China

ISBN: (纸本)9798350302455

In recent years, pre-trained language models have motivated the study in various tasks in natural language processing(NLP). In a new paradigm instead of fine-tuning, reformulating downstream tasks as cloze tasks by add textual or continuous prompts is a popular measure for solving NLP tasks. However, the effect of prompt learning tends to be only ensured by appropriate prompts and precise verbalizers. To address this issue, we propose a prompt matching method based on trigger tokens to solve emotional polar prediction tasks. The proposed model can extract the aspect words and key adjectives from input text as trigger tokens. Meanwhile, by introducing external knowledge we expand trigger tokens which are applicated in verbalizers and prompts matching. According to experiments, we show that providing instances which share the similar emotional trigger tokens improves model performance. And tests on the hotel reviews dataset show that our model have more high precision rate. In few-shot scenario, We also show that the fewer the samples, the more our model outperforms fine-tuning models. These results demonstrate that automatically providing instances with similar emotions is a new method to solve text classification tasks in few-shot scenario. © 2023 IEEE.

关键词： Sentiment analysis

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Boosting Text Classification Performance for Unlabeled Data with Semi-Supervised Learning 9

Boosting Text Classification Performance for Unlabeled Data ...

引用

9th IEEE international Women in engineering (WIE) conference on Electrical and Computer engineering, WIECON-ECE 2023

作者： Khondokar, Tanvina Islam, Saiful Alam Chadni, Tasmia Ishrat Khandokar, Iftakhar Ali United International University Department of Computer Science & Engineering Dhaka Bangladesh

ISBN: (纸本)9798350319651

Text classification has always been an engrossing problem in the field of natural language processing. To build a model on a specific domain, it needs to gain the ability to distinguish text that is related to that domain and others that are not. To make the model achieve this type of domain knowledge, we need to provide a large amount of labeled data within the domain. Having said that, labeling such an amount of data is challenging, as it demands hard labor, a tremendous amount of time, and cost. These could slow the progress of any language or domain model, achieving the performance that is expected. In this paper, we have attempted to address the above-discussed problem, proposing a semi-supervised performance-boosting technique for large-volume unlabeled datasets. The target of this work is to demonstrate the semi-supervised un-labeled data labeling mechanism that can boost the accuracy of text classification models. We have developed an ensemble semi-supervised labeling technique that utilizes the weighted output of different feature-based predictive classifiers to classify unlabelled datasets which makes the labeling process more reliable than unsupervised approaches. The proposed approach overcomes the above-mentioned challenges. After evaluating our approach, we observed that our Semi-Supervised boosting technique enhanced the performance of the predictive models to 20% approx. © 2023 IEEE.

关键词： Large datasets

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Chinese Cases Element Extraction Based on Semantic Dependency and Topic Distribution 3

Chinese Cases Element Extraction Based on Semantic Dependenc...

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3rd international conference on Electronic Information engineering and Computer Science, EIECS 2023

作者： Lin, Weitao Wang, Hao China Telecom Corporation Fujian Branch Fuzhou China University of Electronic Science and Technology of China Institute of Electronic Science and Technology Chengdu China

ISBN: (纸本)9798350316674

To make effective use of the accumulated case knowledge to achieve smarter and fairer justice, this paper proposes a Syntactic analysis and Paragraph Topic based Article Keyword Extraction Model(S-TAKE) and conducts experimental validation to address the problem that mainstream extraction methods do not handle long and multi-topic texts well. The model is divided into two stages: "paragraph keyword extraction"and "chapter keyword screening", and the keywords are extracted at the paragraph and chapter levels, respectively. The keywords obtained can clearly express the secondary themes of the text and help judicial officers quickly access the core content of the text. © 2023 IEEE.

关键词： Chinses cases element extraction component Graphic Models Keywords Extraction natural language processing

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Improving the Extraction of Aspect Terms from Customer Reviews using MSRL-NET and BERT 2

Improving the Extraction of Aspect Terms from Customer Revie...

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

作者： Gupta, Shivani Rakhesh, K. Velmurugan, S. Brindha, T.V. Pranveer Singh Institute of Technology Department of Computer Science and Engineering Kanpur India Shri Jagdishprasad Jhabarmal Tibrewala University Department of Computer Science and Engineering Rajasthan India Rmd Engineering Colleg Department of Information and Technology Kavarapettai Chennai India Sri Venkateshwara College of Engineering Department of Computer Science and Engineering Tiruvallur India

ISBN: (纸本)9798350384277

Aspect Term Extraction (ATE) is a significant task in natural language processing that determinations to normalize and extract particular aspects or properties of things referenced in text. This technique is essential in a dissimilarity of applications, with sentiment analysis, opinion mining, and review summarization. The determination of aspect term extraction is to automatically label and classify words or phrases that express aspects of a specific entity, which could be products, services, or any topic under discussion. In ATE, static word embeddings are required to capture dynamic word meanings properly. We designate a original method for dealing with the intricacies of this difficult and enhancing sentiment analysis accuracy, which uses a Multi-level Semantic Relation-enhanced Learning Network (MSRL-Net) and Bidirectional Encoder Representations from Transformers (BERT). In this paper, we suggest a synergistic framework that uses MSRL-Nets to develop the resolution and clarity of text representations, subsequent by BERT's input format, which is limited to a collection of words and cannot include more context knowledge. MSRL-Net and BERT addresses aspect extraction, sentiment polarity identification, and context-dependent sentiment comprehension by leveraging MSRL-Net's capabilities to create high-resolution text representation and BERT's contextualized language considerate. The combination of MSRL-Nets with BERT designates a promising future path for aspect-based sentiment analysis, with applications such as customer feedback analysis, market research, and social media sentiment monitoring. The experimental evaluation revealed that the proposed technique outperforms the SemEval dataset regarding precision, F1 score, and accuracy. © 2024 IEEE.

关键词： Semantics

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Joint Entity and Relation Extraction with Self-prior Attention for Dual Set Prediction Networks 3rd

Joint Entity and Relation Extraction with Self-prior Attenti...

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3rd international conference on Machine Learning, Cloud Computing and Intelligent Mining, MLCCIM 2024

作者： Wang, Jie Zhang, Juan Shanghai University of Engineering Science Shanghai China

ISBN: (纸本)9789819616930

Joint extraction of entity and relation represents a critical component in natural language processing, exerting a direct influence on the quality of ensuing knowledge graph construction. Although existing methodologies for the joint extraction of entities and relations have demonstrated promising results, exemplified by the parallel triplet decoding method which generates final triplets via ensemble prediction, these approaches often neglect the interactions between entities and relationships. To address this issue, we introduce a dual set prediction network, enhanced with a self-prior attention mechanism (SADSPN).In order to bolster the interactions between entities and relationships, our approach utilizes parallel decoding of triplets via dual set prediction networks, incorporating a self-prior attention mechanism within both networks to intensify the model's emphasis on pivotal information. The initial set prediction network decodes the subject-object information contained within triplets, whereas the second network models the embedded triplets, using the subject-object information to decipher the types of relationships between them. Utilizing a pretrained BERT encoder, our model attains F1 scores of 93.1% and 93.7% on the extensively employed NYT and WebNLG datasets. Additionally, the model exhibits exceptional performance in analyzing overlapping triplets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： knowledge graph

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