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检索条件"任意字段=International Conference on Information Technology - Coding and Computing"

共 195356 条记录，以下是311-320 订阅

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Structural Health Monitoring Data Analysis Using Deep Learning Techniques 24

Structural Health Monitoring Data Analysis Using Deep Learni...

引用

8th international conference on Electronic information technology and Computer Engineering, EITCE 2024

作者： Yang, Linfei Huang, Enzo Faculty of Science and Technology University of Macao China Faculty of Business Administration University of Macao China

ISBN: (纸本)9798400710094

With the rapid development of deep learning technology, its application in the field of Structural Health Monitoring (SHM) represents a significant leap from traditional detection methods. This paper aims to explore how deep learning algorithms enhance the assessment of civil infrastructure integrity by analyzing large datasets from sensors. We first introduce the background of SHM, highlighting the shortcomings of traditional methods in terms of accuracy and efficiency. Next, we employ deep learning models for data analysis, investigating their applications in structural damage detection and prediction. Through a comprehensive analysis of existing literature, we find that deep learning not only overcomes the limitations of manual inspections but also significantly improves the overall performance of SHM by enhancing real-time monitoring capabilities. Furthermore, this paper discusses hybrid systems that combine data-driven models with physical understanding, as well as the distributed intelligence brought about by edge computing. These factors collectively provide more reliable and flexible solutions for future infrastructure management. Our key findings indicate that the application of deep learning can significantly improve the accuracy and efficiency of SHM, enabling a quicker response to the needs of dynamic communities. © 2024 Copyright held by the owner/author(s).

关键词： Federated learning

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Detection of Brain Tumor using Transfer Learning using Conventional Autoencoder with Long Short Term Memory Method 6

Detection of Brain Tumor using Transfer Learning using Conve...

引用

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

作者： Agalya, D. Kamalakkannan, S. Department of Computer Science School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Pallavaram Chennai India Department of Information Technology School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Pallavaram Chennai India

ISBN: (纸本)9798331522667

Brain tumor (BT) has generate a significant health challenge by putting pressure on healthy brain parts or spreading into other areas as well as blocking the flow of fluid around the brain. BT diagnosis is an extensive and time consumption process that depends primarily on radiologists experience and interpretive capabilities. For the area of medical imaging, automatic segmentation of images by means of Deep Learning (DL) approaches. The DL approaches assist in transforming the sector, resulted in improving precision and effectiveness of investigations. However, there are various feature extraction mechanisms available. Therefore, the Recurrent Neural Network (RNN) is utilized for feature extraction and image classification. In model execution, the data have trained in transforming the test image as well as data features for minimizing the domain shift is calculated through the Convolutional Autoencoder (CAE) for reconstruction loss. This research has concentrated in building a model with VGG16 as a single test that subjected at inference and existing method is adopted as neural networks for AE as Transfer Learning (TL) that performs an image analysis task such as segmentation and even set as an adopter for pre training the model. The AE used to train from the source dataset and perform as the adaptors in optimizing during testing using a test subject for effective computation. Moreover, the Long Short Term Memory (LSTM) is utilized as RNN model with CAE for providing improved detection of BT in healthcare industries. Hence, the proposed CAE with LSTM is compared with AE with Convolutional Neural Network (CNN) for evaluating BT detection using MRI dataset with various BT type classifications. © 2025 IEEE.

关键词： Image segmentation

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Cloud Cost Prediction with LSTM on Parametric Data 16th

Cloud Cost Prediction with LSTM on Parametric Data

引用

16th international conference on Genetic and Evolutionary computing, ICGEC 2024

作者： San, Khin Pyae Phyo Thein, Nwe Nwe Myint Data Distribution Management Lab University of Information Technology Parami Road Hlaing Campus Yangon Myanmar Faculty of Computer Science Data Distribution Management Lab University of Information Technology Parami Road Hlaing Campus Yangon Myanmar

ISBN: (纸本)9789819615308

In the distributed computing paradigm, cloud computing provides a wide range of software, platforms and computing infrastructure. It has ability sharable elastic resource pools in dynamic. In this way, cloud computing offers many benefits. It reduces the cost of deployment in applications. It makes easy to increase the capacity of local infrastructures. And it can reduce power consumption by sharing resources. There are challenges over the benefits. The virtual instances needed to be shared equally distributed across different availability zones per demand. That can be categorized into operational and business challenges. In this paper, we implement the cloud prediction system using historical billing data and study the measurement of the model accuracy using Mean Absolute Error and Root Mean Squared Error. The trained model evaluates by the predicted and targeted value graph in this study. By leveraging historical financial data, this method aims to aid organizations in better foreseeing and managing AWS expenditures, leading to improved cost control and financial planning for cloud infrastructure. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cloud platforms

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Advances in smart farming for precision agriculture: Green-IoT and machine learning as a solution 2nd

Advances in smart farming for precision agriculture: Green-I...

引用

2nd international conference on Next-Generation computing and information Systems, ICNGCIS 2023

作者： Digra, Monia Rajput, Preeti Sharma, Pooja Model Institute of Engineering and Technology J&K Jammu India School of Computer Science and Engineering Shri Mata Vaishno Devi University J&K Katra India

ISBN: (纸本)9781032738659

Agricultural development is crucial to ending poverty and boosting economic growth in emerging nations. The global population is expected to reach 9.7 billion by 2050. Smart and precise agriculture must enhance agricultural productivity without compromising quality, nutrition, or natural resources. IoT and AI/ML have significant uses in agriculture due to internet technologies. Although IoT has many benefits, the enormous number of networked devices may raise carbon emissions and e-waste due to energy consumption. Inspired by the goal of sustainable smart agriculture, this article addresses major results and research gaps in IoT-based smart agriculture and provides solutions for energy-saving Green-IoT (G-IoT) applications in agriculture. We start with G-IoT’s evolution and smart agriculture applications. We also examine how green architecture may efficiently apply AI/ML in smart agriculture. We propose an AI/ML G-IoT framework for smart agriculture with green sensor cloud and green AI. This unique G-IoT paradigm provides sustainable and energy-efficient agricultural architecture. We investigate statistical and experimental data and emphasise the limitations of adopting G-IoT and AI/ML architecture in agriculture. In conclusion, G-IoT and AI/ML architecture can enable sustainable, energy-efficient, smart precision agriculture. © 2025 The Author(s).

关键词： Energy utilization

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A digital twin framework for smart contract-based DeFi applications in the metaverse: Towards interoperability, service scaleup & resilience 2nd

A digital twin framework for smart contract-based DeFi appli...

引用

2nd international conference on Next-Generation computing and information Systems, ICNGCIS 2023

作者： Gupta, Ankur Gupta, Surbhi Sharma, Saurabh Department of CSE Model Institute of Engineering and Technology J&K Jammu India

ISBN: (纸本)9781032738659

Decentralized Finance (DeFi) is expected to be the backbone for the metaverse facilitating B2B, B2C, and C2C commerce at scale. Blockchain and smart contracts are key drivers of the DeFi vision, enabling secure and non-repudiable financial transactions based on the fulfillment of pre-defined conditions. However, smart contract business logic can be faulty resulting in erroneous transactions and loss of capital. Testing smart contracts and ensuring 100% correctness also remains a challenge. Finally, to realize the interoperability requirement of the metaverse as mandated by the Metaverse Standards Forum (MSF), service providers shall be required to quickly scale and provide similar services across a plethora of metaverse platforms and ecosystems. Service providers also need to ensure the high availability of their DeFi services and remain resilient in the face of attacks. This research paper presents A Digital Twin Framework for smart contract-based applications for the metaverse, allowing DeFi service providers to create and maintain multiple active and passive twins of their services across metaverse platforms. Passive twins of the service can be used by prospective partners to test their smart contract execution and validate the outcomes. Passive twins can be activated dynamically to improve availability, ensure hyper-scaleup, or respond to attacks. Simulation results establish the effectiveness of the proposed framework laying the foundation for meeting the expectations for the future metaverse. © 2025 The Author(s).

关键词： Smart contract

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Do LLMs Play Dice? Exploring Probability Distribution Sampling in Large Language Models for Behavioral Simulation 31

Do LLMs Play Dice? Exploring Probability Distribution Sampli...

引用

31st international conference on Computational Linguistics, COLING 2025

作者： Gu, Jia Pang, Liang Shen, Huawei Cheng, Xueqi Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798891761964

With the rapid advancement of large language models (LLMs) for handling complex language tasks, an increasing number of studies are employing LLMs as agents to emulate the sequential decision-making processes of humans often represented as Markov decision-making processes (MDPs). The actions in MDPs adhere to specific probability distributions and require iterative sampling. This arouses curiosity regarding the capacity of LLM agents to comprehend probability distributions, thereby guiding the agent's behavioral decision-making through probabilistic sampling and generating behavioral sequences. To answer the above question, we divide the problem into two main aspects: sequence simulation with explicit probability distribution and sequence simulation with implicit probability distribution. Our analysis indicates that LLM agents can understand probabilities, but they struggle with probability sampling. Their ability to perform probabilistic sampling can be improved to some extent by integrating coding tools, but this level of sampling precision still makes it difficult to simulate human behavior as agents. © 2025 Association for Computational Linguistics.

关键词： Markov processes

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A seven layer DNN approach for social-media multilevel image-based text classification 2nd

A seven layer DNN approach for social-media multilevel image...

引用

2nd international conference on Next-Generation computing and information Systems, ICNGCIS 2023

作者： Tripathi, Veena Upadhyay, Navin Mani Sharma, Saurabh Model Institute of Engineering and Technology J&K Jammu India

ISBN: (纸本)9781032738659

In recent years, social media platforms have played a significant role in disaster management. To extract valuable insights and understand the meaning behind social media text content, using conventional machine learning methods, text mining solutions have been created. Through these techniques, messages are intended to be categorized into several themes, such counsel and caution. Nevertheless, these methods frequently focus on a single event and have trouble generalizing to cross events classifications. The effectiveness of traditional models trained on historical data for classifying social media messages from future events remains unclear. Our research focuses on the efficacy of a CNN model in classifying Twitter topics across diverse events. Three geotagged datasets from twitter gathered during the COVID-19 pandemic’s management are the subject of this study. Two conventional machine learning techniques (LR and SVM) are contrasted with the CNN model’s performance. Our experiments conclusively show that the CNN model significantly surpasses the performance of both SVM and LR models in terms of accuracy, regardless of whether the evaluation is based on individual events or across different events. This implies that the CNN model may efficiently categorize messages for impending events and pre-train on Twitter data from past occurrences, improving situational awareness. In conclusion, this study demonstrates the CNN model’s capability compared to traditional machine learning techniques for disaster management. © 2025 The Author(s).

关键词： Disasters

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KrishiAI: Architecture for Harnessing Capabilities of LLMs for Delivery of Accurate Cultivation information to Farmers 2nd

KrishiAI: Architecture for Harnessing Capabilities of LLMs f...

引用

2nd international conference on Agriculture-Centric Computation

作者： Kumar, Mohit Wilson, Shyama Goel, Neeraj Ginige, Athula Indian Inst Technol Ropar India Western Sydney Univ Penrith NSW Australia

ISBN: (纸本)9783031744396;9783031744402

With the constantly evolving agricultural environment, farmers need timely information relevant to their context to make informed decisions. Although several digital solutions have been introduced to address this requirement, these disregard farmers' context in providing information, including information quality. To address this gap, we developed a chatbot in this study incorporating the latest advances in computing techniques. We created an architecture to harness current Large Language Model (LLM) capabilities, including knowledge modeling, to deliver context-specific information to users through questions and answers. The context was established from previous interactions in the form of a dialogue. Neo4J graph database was used to create the knowledge base, ensuring efficient storage and retrieval of contextual information. LLM was used to convert user queries into Cypher queries and to transform knowledge base responses into natural language. A sanitiser module was developed to verify the accuracy of the Cypher queries generated through LLM, significantly enhancing the system's overall reliability. The primary evaluation proved that transitioning from a traditional document-based format to a question-and-answering format and then to a dialogue-based format significantly enhances farmers' ability to access the desired information effectively and promptly.

关键词： LLM Knowledge base Question and Answering GPT

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Cook Smart-An Intelligent Recipe Recommendation System 3

Cook Smart-An Intelligent Recipe Recommendation System

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

作者： Kokate, Vedant Sanjayrao Kunde, Yogesh Babulal Patil, Vedant Prabhakar Futane, Pravin Dedgaonkar, Suruchi Kodmelwar, Manohar Vishwakarma Institute of Information Information Technology Dept. of Information Technology Pune India

ISBN: (纸本)9798331515911

Cook Smart platform offers highly sophisticated personalized diet recommendations powered with the help of individualized nutrition profiles for optimal digestion based on dietary preferences, restrictions or medical conditions. Once the user provides information in terms of weight, height, dietary restrictions and if you are diabetic or hypertensive then CookSmart uses NLP for all this detail to happen also integrate machine learning models, after which our target to calculate a set value that must be used later, whether it predicts your calorie intake. According to the information it displays some custommade recipe suggestions and diet plan which relate with a particular health goal such as weight loss or gain, fitness enhancement in addition to prohibition into certain kind of food stuffs. Html/Css was used to build a simple UI interface when designing the front end of this platform, while in addition Python and Flask based backends that are responsible for data control - processing with backend business *** architecture renders a user experience dynamic in nature that is engine-responsive to be fast and lightweight as it maintains communication with congregated server frontend UI back-end CRUD operations. An interactive, real-time engaging chatbot in the app offers customized meal options and answers nutritional queries. In addition to suggesting meals, CookSmartiap also encourages New Users Save your FavoriteRecipes and Record your Eating History as you set up YOUR individual nutrition goals. In this paper, we describe the design and architecture of CookSmart that enables personalized meal recommendations with nutritional constraints. There is also room for the platform to scale with more complex AI models and an expanded recipe library geared towards individual nutritional requirements as personal nutrition needs gain in popularity in a health-conscious world. © 2025 IEEE.

关键词： Bottles

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Handwritten digit recognition based on SVM 24

Handwritten digit recognition based on SVM

引用

2nd international conference on Frontiers of Intelligent Manufacturing and Automation, CFIMA 2024

作者： Tang, Wendi Fei, Li Beijing Information Science and Technology University Beijing China

ISBN: (纸本)9798400710681

Handwritten digit recognition (HDR) is one of the most classic application scenarios in machine learning and is often employed to validate algorithms and techniques. The task poses multiple challenges due to variations in writing styles and angles. Traditional feature extraction methods are intricate, and machine learning algorithms have difficulty handling complex nonlinear relationships and rely on features. Deep learning algorithms require a considerable amount of data and computing resources, and there exist problems such as gradient disappearance, explosion, and poor interpretability. In this paper, an algorithm model consisting of an encoder, a decoder, and an image classifier is proposed. Features are extracted through convolutional neural networks, image representation is optimized, and multi-classification support vector machines are utilized for classification. In light of the lack of challenge in MNIST datasets, variational autoencoders are utilized to generate numerically extended datasets in diverse styles. Experiments demonstrate that the proposed method outperforms the classical CNN algorithm by nearly 10% on EMNIST datasets, and the accuracy of the generated datasets is 99.4%. At the same time, the influence of different kernel functions on the performance of the model is also explored. © 2024 Copyright held by the owner/author(s).

关键词： Convolutional neural networks

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