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检索条件"任意字段=5th International Conference on Applied Machine Learning and Data Analytics, AMLDA 2022"

共 259 条记录，以下是31-40 订阅

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Research on Intelligent Highway Operation Monitoring and Analysis System 5

Research on Intelligent Highway Operation Monitoring and Ana...

引用

5th international conference on applied machine learning, ICAML 2023

作者： Li, Hao Deng, Jing Yunnan China China Academy of Transportation Sciences Resource Center Beijing China

ISBN: (纸本)9798350341416

With the continuous progress of smart highway construction and the rapid improvement of digitization level, abundant data foundation is provided for road network operation monitoring and comprehensive analysis. In this paper, relying on the digital foundation of smart highways, we design and develop a highway operation monitoring and analysis system with core functions of visualization, measurement, and management. the system aims to achieve functions such as road congestion prediction, event detection and evaluation, and highway operation analysis, supporting the intelligent transformation and high-quality development of highway operation management. © 2023 IEEE.

关键词： data fusion

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Brain MRI Image Classification Using Deep learning 1

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5th international conference on applied machine learning and data analytics, amlda 2022

作者： Meti, Anand Rao, Akanksha Jha, Pratyush KLE Technological University Hubballi Hubballi India

ISBN: (数字)9783031342226

ISBN: (纸本)9783031342219

Brain Tumors can be lethal if not diagnosed or misdiagnosed. Seeing the mortality because of this is high there’s a need to find a solution to detect, classify and diagnose this problem effectively and help medical professionals save precious lives. Brain MRI [1] images help in detection of these tumors. though there are classification techniques the most common limitation is low accuracy. In this paper we use Deep learning Architectures to perform these tasks. Architectures like VGG19, ResNet50 [2], DenseNet201 [4] and Xception [5] which are Convolutional Neural Networks [6] are analysed and compared and a novel model is built. We used the BraTS2020 [3] dataset which consists of images of four types Pituitary tumor, Meningioma tumor, Glioma tumor and no tumor we next performed resizing, re-scaling, preprocessing techniques on them. Following which we deployed the architecture to obtain our first set of results i.e, VGG19 gave accuracy of 70%, ResNet gave 72%, Xception gave 75% and DensetNet201 gave 76%. To make our accuracies better we performed image enhancement. [7]the process where adjusting of digital images takes place to ensure that the results are more suitable to display or for doing further image analysis is called image enhancement. three types of Image enhancement techniques have been used: Adaptive thresholding [8], Brightening [9], Contrasting [10] and we found that with contrasting we got the best results and VGG19 architecture gave highest accuracy of 83% proving the importance of image enhancement. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Image enhancement

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Ensemble learning Approach for Short-term Energy Consumption Prediction 2022

Ensemble Learning Approach for Short-term Energy Consumption...

引用

5th ACM India Joint international conference on data Science and Management of data (CoDS-COMAD) / 9th ACM IKDD CODS conference / 26th COMAD conference

作者： Reddy, Sujan A. Akashdeep, S. Harshvardhan, R. Kamath, Sowmya S. Natl Inst Technol Karnataka Dept Informat Technol Surathkal Karnataka India

ISBN: (纸本)9781450385824

Predicting electricity consumption accurately is crucial for garnering insights and potential trends into energy consumption for effective resource management. Due to the linearity/non-linearity in usage patterns, electricity consumption prediction is challenging and cannot be adequately solved by using a single model. In this paper, we propose ensemble learning based approaches for short-term electricity consumption on an open dataset. the ensemble model is built on the combined predictions of supervised machine learning and deep learning base models. Experimental validation showed that the proposed ensemble model is more accurate and decreases the training time of the second layer of the ensemble by a factor close to ten, compared to the state-of-the-art. We observed a reduction of approximately 34% in the Root mean squared error for the same size of historical window.

关键词： Energy forecasting machine learning Ensemble learning Predictive analytics

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An Abnormal Detection and Early Warning System for Sugarcane Diseases Based on Unsupervised learning 5

An Abnormal Detection and Early Warning System for Sugarcane...

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5th international conference on applied machine learning, ICAML 2023

作者： Li, Dongrui Qiu, Shangming Yang, Shanyou Guangdong Agriculture Industry Business Polytechnic Guangzhou510507 China School of Computer Guangdong Agriculture Industry Business Polytechnic Guangzhou510507 China

ISBN: (纸本)9798350341416

Considering the problem of scarce annotation data and diverse disease types in sugarcane disease detection, this paper proposes a detection and early warning system for sugarcane disease abnormalities based on unsupervised learning features. First, two unsupervised learning methods, clustering and self-encoder, are used for feature extraction, and then these features are fused to form a comprehensive feature vector. then, the local anomaly factor (LOF) algorithm is used to detect the abnormal condition of sugarcane diseases. the experimental results show that compared with the traditional supervised learning method, the proposed method has a significant improvement in both accuracy and recall rate, which effectively solves the problem of insufficient annotation data in sugarcane disease detection. this method has high accuracy and stability in the abnormal detection and early warning of sugarcane diseases, and can provide strong support for the prevention and control of sugarcane diseases. © 2023 IEEE.

关键词： Unsupervised learning

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Multimodal Medical Image Fusion Using Deeplearning Models in Fuzzy Domain 5

Multimodal Medical Image Fusion Using DeepLearning Models in...

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5th IEEE international conference on Cybernetics, Cognition and machine learning Applications, ICCCMLA 2023

作者： Jana, Mayukhmala Das, Arpita University of Calcutta Dept. of Radio Physics & Electronics Kolkata India

ISBN: (纸本)9798350338287

In disease diagnosis, where a significant amount of medical data is currently being established, multi-modal medical image fusion is essential to reduce the diagnosis time and data. Using unsupervised learning based fuzzy decomposition rule followed by recent deep-learning models as feature extractor, this study presents a novel fusion algorithm. this algorithm uses the extracted stacked features to create significant weights from perspective of spatial and frequency spectrum of the decomposed subimages. To fuse those subimages some weighted fusion rules are applied, which can preserve all delicate details from the source images. the dataset used in this work are from radiological and pathological point of view. the comparative study of the proposed method with recent models shows better qualitative and quantitative results. © 2023 IEEE.

关键词： Deep learning

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A Short Review on Cataract Detection and Classification Approaches Using Distinct Ophthalmic Imaging Modalities 10th

A Short Review on Cataract Detection and Classification Appr...

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10th international conference on Big data analytics (BDA)

作者： Garg, Aakash Yadav, Jay Kant Pratap Singh Yadav, Sunita Ajay Kumar Garg Engn Coll Ghaziabad India

ISBN: (纸本)9783031283499;9783031283505

A cataract is one of the leading causes of visual impairment worldwide compared with other major age-related eye diseases, including blindness, such as diabetic retinopathy, age-related macular degeneration, trachoma, and glaucoma. Cloudiness in the lens of an eye leads to an increasingly blurred vision where genetics and aging are the leading cause of cataracts. In recent years, various researchers have shown an interest in developing state-of-the-art machine learning and deep learning techniques-based methods that work on distinct ophthalmic imaging modalities aiming to detect and prevent cataracts in the early stage. this survey highlights the advances in machine learning and deep learning state-of-the-art algorithms and techniques applied to cataract detection and classification using slit lamps, fundus retinal images, and digital camera images. In addition, this survey also provides insights into previous works along with the merits and demerits.

关键词： Cataract Visual impairment Ophthalmic imaging modalities machine learning Deep learning

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data-Driven Approach: Predicting Critical Braking Temperature in Train Wheels Using Fiber Bragg Grating Sensors 5

Data-Driven Approach: Predicting Critical Braking Temperatur...

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5th IEEE international conference on Communication, Computing and Industry 6.0, C2I6 2024

作者： Sigtia, Sumith Gayathri, H.N. Mehta, Vidhan Deepa, N. Sharma, Sneha Sharan, Preeta The Oxford College of Engineering Dept. of Ai & Ml Bangalore India The Oxford College of Engineering Dept. of Chemistry Bangalore India Dayananda Sagar University Dept. of Electronics and Communication Bangalore India Ece the Oxford College of Engineering Dean R&d Bangalore India

ISBN: (纸本)9798350379884

this research presents a novel approach to predicting the critical temperature at which brakes must be applied to train wheels, combining real-time data collection through Fiber Bragg Grating (FBG) sensors with advanced machine learning models. While previous studies have either focused on theoretical models or utilized real-time sensor data without predictive capabilities, our approach uniquely integrates both sensor data and predictive modeling to improve accuracy. Conducted at the Oxford College of Engineering, our experiment used a scaled-down train model to measure key parameters such as speed, weight, and temperature during braking. Unlike traditional models, our study employed machine learning algorithms, including Linear Regression, Decision Tree, and Random Forest, to forecast the critical braking temperature. the Random Forest model significantly outperformed other models, achieving a perfect R2 score of 1.00 and a Mean Squared Error (MSE) of 0.00. Our integrated approach not only enhances the precision of braking temperature predictions but also offers a robust framework for predictive maintenance, improving the operational safety and efficiency of rail systems. this research marks a significant step forward in real-time monitoring and predictive analytics for rail transport systems, providing a superior method for detecting and responding to critical thermal conditions. © 2024 IEEE.

关键词： Fiber Bragg gratings

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machine learning Based Problem Solving Approach in Green Computing 5

Machine Learning Based Problem Solving Approach in Green Com...

引用

5th international conference on Emerging Smart Computing and Informatics, ESCI 2023

作者： Bakre, Shashikant Shiralkar, Ashpana Shelar, Sachin V. Ingle, Suchita Aissms Ioit Electrical Engineering Department Pune India

ISBN: (纸本)9781665475242

the issues related to conventional generation of electricity arethe matter of concern for power sector today. these include diminishing stock of coal over a period of time, unavailability of good quality coal, non-sustainable issues, ash handling problems etc. Green energy is the alternative to overcome these problems. the green energy is sustainable, renewable and economical. In India, the existing ratio of conventional to non-conventional generation as on 30th June 2022 is 72:28%. It is required to further improve this ratio to the tune of 60:40%. the performance of the green energy systems can be optimized by AI ML based green computing. Under the umbrella of AI, several technologies have been emerged. these technologies are machine learning, deep learning, data analytics, robotics, neural networks, expert systems, fuzzy logic systems, natural language processing, genetic algorithms etc. the green computing can be made more effective through research as regards how to use these technologies. In this paper, a novice techniques of AI ML based green computing have been proposed. Python programming language is used as a back end programming tool. the proposed methods are simple, cost effective and feasible. © 2023 IEEE.

关键词： data analytics

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Prediction of Covid-19 Recovery Chances Using Supervised machine learning Classification 5th

Prediction of Covid-19 Recovery Chances Using Supervised Mac...

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5th international conference on Innovative Product Design and Intelligent Manufacturing Systems, ICIPDIMS 2023

作者： Dhanalakshmi, R. Nivashini, A. Vijayaraghavan, N. Centre for Cyber Physical Systems School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department of Computer Science and Engineering MNM Jain Engineering College Chennai India Department of Mathematics KCG College of Technology Chennai India

ISBN: (纸本)9789819767311

Predictive analytics if combined with machine learning approaches have the potential to play a significant role in forecasting the spread of respiratory infections. machine learning approaches aid in the mining of data to better assess and forecast the presence or absence of covid-19 infection. In this paper, XGBoost Ensemble Classification using a fine-tuned approach is being used. Various machine learning algorithms are being analyzed and XGBoost Ensemble Classification algorithm is used to forecast and predict the covid-19 infected patient recovery chances that is the possible outcome of a covid-19 infected patient along with the prediction probability of recovery. the suggested categorization model is applied to a variety of situations. When compared to other ensemble models like random forest, hybrid ensemble and other individual classifiers such as decision tree, logistic regression, K-nearest neighbor, support vector machine and Naive Bayes, the XGBoost Ensemble Classification model gave the highest accuracy. Predicting the correct class is important. thus, the proposed system discusses the possibility of automated prediction for covid-19 infected patient recovery chances along with the probability of recovery and the effectiveness of such predictions. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： COVID-19

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Aspect-Based Sentiment Classification of Online Product Reviews Using Hybrid Lexicon-machine learning Approach 1

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6th international conference on applied machine learning and data analytics, amlda 2023

作者： Asuquo, Daniel Attai, Kingsley Usip, Patience George, Uduak Osang, Francis Department of Computer Science Faculty of Science University of Uyo Uyo Nigeria TETFund Center of Excellence in Computational Intelligence Research UNIUYO Uyo Nigeria Department of Mathematics and Computer Science Ritman University IkotEkpene Ikot Ekpene Nigeria Department of Computer Science National Open University of Nigeria Abuja Nigeria

ISBN: (数字)9783031554865

ISBN: (纸本)9783031554858

Nowadays, a good number of customers express their experience with online products. these reviews have an important role in customers’ purchase decision process. there may be hundreds or thousands of unstructured and heterogeneous reviews for a popular product. Traditional text processing techniques have limited capability in extracting opinions on customers’ product reviews from huge data over the Internet. Although lexical approaches aim to map words to sentiments by building a lexicon, the process of developing a lexicon with sentiment scores for phrases and sentences becomes tedious and time consuming as data volume increases. Currently, text sentiment analysis requires fast and accurate techniques to decode and quantify the emotion in tweets. this paper presents a hybrid framework based on lexicon and machine learning (ML) algorithms to train previously seen tweets in order to predict the sentiments of some new input tweets into positive, negative, and neutral polarities. Tweepy library was used to extract tweets on Laptop reviews to identify some aspects and classify sentiments towards them into specific polarity. After data pre-processing, the implementation in Python used Natural Language Processing package called TextBlob to assign subjectivity and polarity scores to text. the scores were used by the ML algorithms to analyze and classify sentiments. A dataset of 2226 tweets was used for training and testing Support Vector machine, Random Forest, and Naïve Bayes classifiers. Results indicate that Random Forest classifier outperforms others in the task of classifying sentiments on Laptop reviews with the highest accuracy (96%), precision (97%), and F1-score (96%). © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Python

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