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检索条件"任意字段=3rd International Conference on Intelligent Data Engineering and Automated Learning"

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engineering Carbon Emission-aware Machine learning Pipelines 24

Engineering Carbon Emission-aware Machine Learning Pipelines

引用

IEEE/ACM 3rd international conference on AI engineering - Software engineering for AI (CAIN)

作者： Husom, Erik Johannes Sen, Sagar Goknil, Arda SINTEF Oslo Norway

ISBN: (纸本)9798400705915

The proliferation of machine learning (ML) has brought unprecedented advancements in technology, but it has also raised concerns about its environmental impact, particularly concerning carbon emissions. To address the imperative of environmentally responsible ML, we present in this paper a novel ML pipeline, named CEMAI, designed to monitor and analyze carbon emissions across the entire lifecycle of ML model development, from data preparation to training and deployment. Our endeavor involves an exhaustive evaluation process underpinned by three industrial case studies. These case studies are structured around the application of ML models to predict tool wear, estimate remaining useful lifetimes, and detect anomalies in the Industrial Internet of Things (IIoT). Leveraging sensor data originating from CNC machining and broaching operations, our research shows empirically the efficacy of carbon emissions as a dependable metric guiding the configuration of an ML development process. The essence of our approach lies in striking a balance between superior performance and minimal carbon emissions. Our findings reveal the potential to optimize pipeline configurations for ML models in a manner that not only enhances performance but also drastically reduces carbon emissions, thereby underlining the significance of adopting ecologically responsible engineering practices.

关键词： Machine learning

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From Manual to Machine: How AI is Redefining Web Scraping for Superior Efficiency: A Literature Review 3

From Manual to Machine: How AI is Redefining Web Scraping fo...

引用

3rd international conference on Communication, Control, and intelligent Systems, CCIS 2024

作者： Ayuso, Enrique Dumfeh Brogya, Maame Serwah Kumar Ahlawat, Vinay Sain, Mangal Dongseo University Dept. of Computer Engineering Busan Korea Republic of Gla University Dept. of Electronics & Communication Mathura India

ISBN: (纸本)9798331528201

Web Scraping involves the use of bots for the purpose of extracting data from the online web. To extract such data, the web scraper must conduct at least 3 different steps, i.e., collect the necessary links from the web, extract data using the provided links, and save the information in a neatly organized CSV file or database. However, traditional methods to accomplish these steps have faced many difficulties, such as anti-scraping measures and website changes. To tackle these difficulties and to accomplish the steps in a more rapid yet advanced way, Artificial Intelligence (AI) can be utilized. As of mid-2024, the rapid development and improvement of AI has resulted in its integration into many businesses and educational institutions. The current state of AI is remarkably advanced that it can be integrated in about any field and sector available. In this paper, the researcher defined the term Web Scraping, Artificial Intelligence, Natural Language Processing, and Machine learning, explained the use of AI in Web Scraping including its importance and potential, and the understanding of ethical web scraping practices. This literature review indicated that the integrated use of AI improves the performance of Web Scraping compared to traditional methods. © 2024 IEEE.

关键词： Adversarial machine learning

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Transforming Agriculture with Cloud Computing: data Processing and Analysis Innovations 3

Transforming Agriculture with Cloud Computing: Data Processi...

引用

3rd international conference on intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Mohammed, Shehu Singh, Arun Kaur, Harpreet Lovely Professional University Department of Computer Application Phagwara India Lovely Professional University Department of Computer Science and Engineering Phagwara India Lovely Professional University School of Computer Science and Engineering Phagwara India

ISBN: (纸本)9798331515911

Cloud computing is a game-changer in modern agriculture, providing new forms to access enormous data analysis. In this paper, we investigate how cloud computing solutions are being integrated with agricultural practices to improve the efficiency, scalability and availability in managing data pertaining to agriculture. Farming operations generate massive amounts of data from a multitude of sources like sensors, drones, and satellite imagery., the performance bottleneck becomes the traditional methods with which the data are being processed. So the common factor in all these problems lies with Cloud computing itself, it has a scalable infrastructure that provides reliable analysis and Real-time processing of large data sets throwing light on an array of questions people have about various aspects. In this paper, we explore the major innovations in cloud computing that power precise agriculture and examine how machine learning algorithms, data fusion methods, and distributed processing platforms are utilized. It also discusses cloud-based solutions, which can reduce costs of the use of resources, crop yield prediction, and risk management to weather/climate and pests. Cloud computing opens up a world of monitoring, analysis, and optimization to help provide higher yields more efficiently while maintaining a commitment to sustainable agriculture. Challenges and considerations in deploying cloud computing in agriculture, e.g., data security risks, privacy concerns, and the need for stable internet connection for rural environments are also articulated in the paper. The results of this project underscore the vital importance of cloud technology in redefining the agri-future and providing a robust platform for smarter and more sustainable agriculture. © 2025 IEEE.

关键词： agricultural data processing Cloud computing data analysis machine learning precision agriculture sustainable farming

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Automatic PDF Document Classification with Machine learning 25th

Automatic PDF Document Classification with Machine Learning

引用

25th international conference on intelligent data engineering and automated learning

作者： Llacer Luna, Socrates Garigliotti, Dario Martinez Plumed, Fernando Ferri Ramirez, Cesar Univ Politecn Valencia Valencia Spain Univ Bergen Bergen Norway

ISBN: (纸本)9783031777301;9783031777318

Universitat Polit`ecnica de Val`encia (UPV) faces challenges in managing its Alfresco document repository, which contains 600,000 PDF files, of which only 100,000 are correctly categorised. Manual classification is laborious and error-prone, hindering information retrieval and advanced search capabilities. This project presents an automated pipeline that integrates optical character recognition (OCR) and machine learning to efficiently classify documents. Our approach distinguishes between scanned and digital documents, accurately extracts text and categorises it into 51 predefined categories using models such as BERT and RF. By improving document organisation and accessibility, this work optimises UPV's document management and paves the way for advanced search technologies and real-time classification systems.

关键词： Document Classification OCR Machine learning Alfresco Repository

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Surface Crack Identification in Concrete Structures through Convolutional Neural Networks 3

Surface Crack Identification in Concrete Structures through ...

引用

3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Jency, S. Ramkumar, G. Saveetha University Saveetha School of Engineering Dept of Electronics and Communication Engineering Tamil Nadu Chennai602105 India

ISBN: (纸本)9798331509675

Surface crack detection in concrete structures is crucial for maintaining infrastructure integrity and safety. This paper presents a deep learning approach leveraging Convolutional Neural Networks (CNNs) for automated crack detection. We propose a CNN-based model that processes high-resolution images of concrete surfaces to identify and classify cracks. The dataset comprises 40,000 images, labelled as either containing positive or negative, and resized to 224x224 pixels. The model architecture consists of several convolutional layers for feature extraction. These convolutional layers are designed to identify key patterns in the input data. After feature extraction, max-pooling layers are applied to reduce the dimensionality of the data. This dimensionality reduction helps in simplifying the learned features while retaining important information. Finally, dense layers are used for classification tasks. The dense layers process the extracted features to make predictions or classifications based on the learned patterns. Training is performed on 80% of the dataset with data augmentation techniques to improve generalization, and the remaining 20% is used for validation. Our model achieves an accuracy of 98.92% and a recall of 94.32% on the validation set, demonstrating its effectiveness in detecting surface cracks. The results indicate that CNNs can significantly enhance the accuracy and efficiency of crack detection compared to traditional methods. This study underscores the potential of deep learning for automating infrastructure inspection processes and sets the stage for future research into more advanced models and larger datasets. © 2025 IEEE.

关键词： Dimensionality reduction

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Automotive Logistics Transportation Path Planning System on Basis of Artificial Intelligence 3

Automotive Logistics Transportation Path Planning System on ...

引用

3rd international conference on data Analytics, Computing and Artificial Intelligence, ICDACAI 2024

作者： Yu, Qianying Xiang, Chengjiu Su, Li School of Economics and Management Liaoning Agricultural Technical College Liaoning Yingkou115009 China Modern Service Teaching Department Yingkou Agricultural Engineering School Liaoning Yingkou115009 China

ISBN: (纸本)9798331519032

With the continuous growth of market demand and express delivery business, logistics enterprises must build a more intelligent logistics system. This article adopted a deep learning based AI (Artificial Intelligence) method to plan the route of automobiles. The optimization module of the automobile logistics transportation path planning system can continuously adjust the results of path planning based on real-time data and actual needs, thereby improving transportation efficiency and reducing costs. On this basis, this article studied the trajectory planning results of deep learning based trajectory planning algorithms and traditional algorithms under different load conditions. Method 3 (medium load+traditional method) Path length: 12km, transportation time: 20min;Method 4 (medium load+deep learning method) Path length: 11 km, transportation time: 17 minutes. In view of an AI based automotive logistics transportation path planning system, This article' system is beneficial for improving logistics transportation speed and shortening logistics transportation time. © 2024 IEEE.

关键词： Motion planning

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Predicting Road Accident Severity and Identifying High-Risk Areas Using Tree-based learning: A Case Study from the UK 3

Predicting Road Accident Severity and Identifying High-Risk ...

引用

3rd international conference on Communication, Control, and intelligent Systems, CCIS 2024

作者： Akarsha, D.P. Mohan, S.G. Nagendra Bharadwaj, D.G. Pandu Kumar, A.N. Manoj, V.S. Nitte Meenakshi Institute of Technology Department of Information Sciences and Engineering Bengaluru India

ISBN: (数字)9798331528201

ISBN: (纸本)9798331528201

Road accidents are a pressing global issue, often resulting in injuries ranging from minor to fatal. This study leverages machine learning algorithms to predict the severity and location of road accidents, with the aim of enhancing road safety measures. Utilizing accident data from the UK, we developed predictive models that classify the severity of accidents on a scale from 1 to 5, where 1 represents the least severe and 5 represents the most severe outcomes. The model's predictions have been integrated into a user-friendly application, providing visual insights into accident severity and highlighting high-risk areas on a map. By identifying and visualizing accident-prone locations, this study offers a valuable tool for authorities to implement targeted interventions, potentially reducing the frequency and impact of road accidents. The findings underscore the potential of machine learning in proactive road safety management, contributing to efforts that could save lives and minimize the societal costs associated with road accidents. © 2024 IEEE.

关键词： Adaptation models Visualization Road accidents Machine learning algorithms Costs Machine learning Predictive models Road safety data models Frequency measurement

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Ensembled Machine learning Models for Antenna Optimization in Wireless Communication/ Biomedical Applications 3

Ensembled Machine Learning Models for Antenna Optimization i...

引用

3rd international conference on Communication, Control, and intelligent Systems, CCIS 2024

作者： Upadhyay, Nidhi Kaur, Amanpreet Singh, Ashima Pattanayak, Arnab Thapar Institute of Engineering and Technology Department of Electronics and Communication Engineering Punjab India Thapar Institute of Engineering and Technology Department of Computer Science Engineering Punjab India

ISBN: (纸本)9798331528201

In this work, the antenna optimisation process is done by using ML algorithms such as KNN, random forest, Gradient Boost, XG Boost and MLP. Then the predicted results are ensembled using Voting Regressor. A Cylindrical Dielectric Resonator Antenna (CDRA) was designed and simulated in CST MWS'23 for this purpose. dataset is created by varying the radius of the DR using parametric sweep function of the EM simulator. A close prediction between actual values and the predicted ones is obtained using these ML models. It is observed that by ensembling these varied approaches, a model that can handle different types of relationships in the data, can be created. This algorithm can be further utilized for optimisation of other antenna parameters and thus helping in reducing time in optimising complex antenna designs. The accuracy achieved by the ensemble model is 98.6% while maintaining a small MSE of 0.001. The optimized design shows stable characteristics within the ISM band and hence can be used for biomedical as well as wireless communication applications. © 2024 IEEE.

关键词： Random forests

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Breast Histopathology Image Classification: A Comparative Evaluation of SVM, Random Forest and CNN 3

Breast Histopathology Image Classification: A Comparative Ev...

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3rd IEEE international conference on intelligent Technologies, CONIT 2023

作者： Yogitha Rachana, P. Milina, M. Sabhahit, Neel Ganapathi Ainain, Mohammad New Horizon College of Engineering Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350338607

Artificial intelligence (AI) uses the impressions of human intelligence to wire machines to think and mimic the human behaviour. Deep learning, Machine learning, Neural Networks, are a few concepts included in AI among many others. Convolutional neural network (ConvNet or CNN) is a category of machine learning commonly applied for image recognition and processing pixel data. Support Vector Machine (SVM) algorithm is a Supervised learning algorithm used often for Regression problems and Classification problems. Random Forest is a collaborative algorithm which is also used for both Regression and Classification problems. This paper provides a comparative analysis of CNN, SVM and Random Forest Algorithms. The dataset used is the Breast Histopathology Image dataset. © 2023 IEEE.

关键词： Support vector machines

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Maximizing Throughput and Minimizing Latency: Evolutionary Algorithms for Optimizing Wireless Cellular Networks 3

Maximizing Throughput and Minimizing Latency: Evolutionary A...

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3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Pechetti, Jyoshitha Yadav, Sneha Tarunya, G. Rajagopal, Shinu M. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

In the rapidly evolving world of wireless cellular network, optimizing key parameters like data throughput and latency is of critical importance for ensuring high quality communication services. The proposed presents a novel approach to multi-objective optimization to enhance overall network performance using Genetic Algorithms (GA), Pareto, and Particle Swarm Optimization (PSO). By employing optimization algorithms, a balance is sought in terms of trade-off of other parameters while maximizing data throughput and minimizing latency. Simulation layers indicate the comparative study further solidifies the efficacy of the value of evolutionary algorithms in optimizing wireless networks to satisfy the growing needs of high speed and less latency services in today’s communication landscape. © 2025 IEEE.

关键词： Optimization algorithms

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