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检索条件"任意字段=5th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2004"

共 181 条记录，以下是31-40 订阅

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5th international conference on Recent Innovations in Computing , ICRIC 2022

5th International Conference on Recent Innovations in Comput...

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5th international conference on Recent Innovations in Computing , ICRIC 2022

ISBN: (纸本)9789819906000

the proceedings contain 66 papers. the special focus in this conference is on Recent Innovations in Computing. the topics include: the Importance of Selected LMS Logs Pre-processing Tasks on the Performance Metrics of Classification Models;analysis of Deep Pre-trained Models for Computer Vision Applications: Dog Breed Classification;an Efficient Recognition and Classification System for Paddy Leaf Disease Using Naïve Bayes with Optimization Algorithm;development of Decision-Making Prediction Model for Loan Eligibility Using Supervised Machine learning;prediction of Osteoporosis Using Artificial Intelligence Techniques: A Review;analyzing Stock Market with Machine learning Techniques;machine learning Techniques for Image Manipulation Detection: A Review and Analysis;improving Real-Time intelligent Transportation Systems in Predicting Road Accident;Consumer Buying Behavior Analysis During COVID-19 Phase Using Statistical data Mining and Multi-verse Stochastic Algorithm;clinical Named Entity Recognition Using U-Net Classification Model;addressing Role of Ambient Intelligence and Pervasive Computing in Today’s World;Improving the AODV Routing Protocol Against Network Layer Attacks Using AntNet Algorithm in VANETs;a Comparative Study of IoT-Based automated Hydroponic Smart Farms: An Urban Farming Perspective;Smart Healthcare System Based on AIoT Emerging Technologies: A Brief Review;intelligent Communication for Internet of things (IoRT);energy-Efficient Priority-Based Routing Model for Smart Health Care;An Internet of things-Based Mining Worker Safety Helmet Using ESP32 and Blynk App;Time Series-Based IDS for Detecting Botnet Attacks in IoT and Embedded Devices;swarm Intelligence-Based Energy-Efficient Framework in IoT;Performance Analysis of MADM Techniques in Cognitive Radio for Proximity Services;neural Machine Translation from English to Marathi Using Various Techniques.

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Strawberry Maturity Detection Algorithm Based on Improved YOLOv9 5

Strawberry Maturity Detection Algorithm Based on Improved YO...

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5th international conference on Advances in Electrical engineering and Computer Applications, AEECA 2024

作者： Ye, Rong Shao, Guoqi Zhang, Hongrui Li, Tong Gao, Quan Yunnan Agricultural University College of Food Science and Technology Kunming650201 China Yunnan Agricultural University The Key Laboratory for Crop Production and Smart Agriculture of Yunnan Province Kunming650201 China Yunnan Agricultural University College of Landscape and Horticulture Kunming650201 China Yunnan Agricultural University College of Big Data Kunming650201 China

ISBN: (纸本)9798350355253

Deep learning is a crucial research focus within the realm of artificial intelligence, with its utilization in agriculture becoming more prevalent. Strawberries are one of the most popular fruits, but immature strawberries are difficult to attract consumers. this paper aims to enhance the accuracy of strawberry ripeness detection during the picking process by introducing a strawberry ripeness detection model called YOLOv9-CS. the model integrates Cloformer into the backbone network to improve the fusion effect of texture features among strawberries of the same maturity. Additionally, the inclusion of SeaFormer Block in the neck network enhances the model's ability to perceive target positions, thereby boosting detection accuracy. By utilizing the Focal-EIoU loss function instead of the traditional IoU loss function, the model's robustness and accuracy are further improved. Experimental results on a strawberry dataset demonstrate that the YOLOv9-CS model outperforms the original YOLOv9 model, achieving a 2.47% increase in precision, 4.54% in recall, and 4.16% in mAP, reaching 95.72%, 95.59%, and 96.61% respectively, while maintaining real-time detection capabilities. the proposed model significantly enhances the identification of strawberries at different maturity levels in natural settings, catering to the intelligent and automated requirements of smart agriculture for strawberry picking. © 2024 IEEE.

关键词： Fruits

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Sum-Product Network-Based Cardinality Estimation Research 5th

Sum-Product Network-Based Cardinality Estimation Research

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5th international conference on Artificial Intelligence in China, AIC 2023

作者： Tie, Jun Yang, Hanchao Cheng, Yong Sun, Chong Pan, Ziyi College of Computer Science South-Central Minzu University Wuhan430074 China Hubei Provincial Engineering Research Center for Intelligent Management of Manufacturing Enterprises Wuhan430074 China Hubei Provincial Engineering Research Centre for Agricultural Blockchain and Intelligent Management Wuhan430074 China Beijing Qingtai Data Technology Co. Ltd. Beijing100000 China

ISBN: (纸本)9789819975440

As data distribution is non-uniform and attribute correlations vary in strength, challenges are encountered when using learning-based cardinality estimation methods in database query optimization. An improved algorithm based on the sum-product network was proposed to address these issues. the algorithm has two improvements: a self-adaptive clustering selection algorithm (SCS) for the clustering algorithm module and an RDC-based automated Search for a Hyperparameter Tuning method (RDC-ASHT) for the attribute partitioning module. SCS selects the best clustering method, improves the ability to partition and merge clusters, and validates the performance of the grid-based clustering method. RDC-ASHT searches for optimal threshold intervals, reducing computational resources and time costs and enabling faster discovery of the optimal solution. Experimental results demonstrate that the improved model has better speed and generalization performance than the original and can accurately estimate cardinalities. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Clustering algorithms

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Evaluation Toolkit For Robustness Testing Of Automatic Essay Scoring Systems 2022

Evaluation Toolkit For Robustness Testing Of Automatic Essay...

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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： Kabra, Anubha Bhatia, Mehar Singla, Yaman Kumar Li, Junyi Jessy Shah, Rajiv Ratn Adobe San Jose CA 95110 USA IIIT Delhi Delhi India SUNY Buffalo Buffalo NY USA Univ Texas Austin Austin TX 78712 USA

ISBN: (纸本)9781450385824

Automatic scoring engines have been used for scoring approximately fifteen million test-takers in just the last three years. this number is increasing further due to COVID-19 and the associated automation of education and testing. Yet, the AI-based testing literature of these 'intelligent' models is highly lacking as most of the papers propose new models that rely only on quadratic weighted kappa (QWK) based agreement with human raters for showing model efficacy. However, this effectively ignores the highly multi-feature nature of essay scoring. Essay scoring depends on features like coherence, grammar, relevance, etc. and to date, there has been no study testing automated Essay Scoring (AES) systems holistically on all these features. With this motivation, we propose a model agnostic adversarial evaluation scheme and associated metrics for AES systems to test their natural language understanding capabilities and overall robustness. We evaluate the current state-of-the-art AES models using the proposed scheme and report the results on five recent models. these models range from feature-engineering-based approaches to the latest deep learning algorithms. We find that AES models are highly overstable such that even heavy modifications (as much as 25%) with content unrelated to the topic of the questions do not decrease the score produced by the models. On the other hand, unrelated content, on average, increases the scores, thus showing that the models' evaluation strategy and rubrics should be reconsidered. We conduct a human survey with 200 human raters and observe that they can easily detect differences between the original and perturbed responses and have a general disagreement with the scores assigned by auto scorers.

关键词： Deep learning

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Plant Disease Detection and Classification Using a Deep learning-Based Framework 24th

Plant Disease Detection and Classification Using a Deep Lear...

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24th international conference on intelligent data engineering and automated learning, ideal 2023

作者： Ghosh, Mridul Lasker, Asifuzzaman Banerjee, Poushali Manna, Anindita Obaidullah, Sk Md Gonçalves, Teresa Roy, Kaushik Department of Computer Science ShyampurSiddheswariMahavidyalaya Howrah India Department of Computer Science and Engineering Aliah University Kolkata India Department of Computer Science Surendranath College Kolkata India Departamento de Informática Universidade de Évora Evora Portugal Department of Computer Science West Bengal State University Kolkata India

ISBN: (纸本)9783031482311

Plant diseases pose a significant threat to agriculture, causing substantial yield losses and economic damages worldwide. Traditional methods for detecting plant diseases are often time-consuming and require expert knowledge. In recent years, deep learning-based approaches have demonstrated great potential in the detection and classification of plant diseases. In this paper, we propose a Convolutional Neural Network (CNN) based framework for identifying 15 categories of plant leaf diseases, focusing on Tomato, Potato, and Bell pepper as the target plants. For our experiments, we utilized the publicly available PlantVillage dataset. the choice of a CNN for this task is justified by its recognition as one of the most popular and effective deep learning methods, especially for processing spatial data like images of plant leaves. We evaluated the performance of our model using various performance metrics, including accuracy, precision, recall, and F1-score. Our findings indicate that our approach outperforms state-of-the-art techniques, yielding encouraging results in terms of disease identification accuracy and classification precision. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： data handling

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IoT-Enabled intelligent System for Helmet Detection and automated Penalty Enforcement using AI & ML

IoT-Enabled Intelligent System for Helmet Detection and Auto...

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Trends in Material Science and Inventive Materials (ICTMIM), international conference on

作者： P. Vishnu Kumar Nandasree Shathanakota Saimanaswini Rachamadugu Uppara Layavardhini Department of Internet of Things G. Pullaiah College of Engineering and Technology Kurnool Andhra Pradesh India

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

the increasing number of road accidents due to non-compliance with helmet regulations highlights the need for intelligent systems to enhance rider safety. this paper presents an IoT-enabled system that utilizes Artificial Intelligence (AI) and Machine learning (ML) to detect riders without helmets and enforce penalties automatically. the system integrates a Raspberry Pi with a camera module to capture real-time images of motorcyclists at traffic signals. AI-powered object detection models, such as YOLOv5, determine helmet usage, while Optical Character Recognition (OCR) techniques extract license plate details for violators. the system transmits violation data to a centralized server via IoT communication protocols, where an automated penalty system processes the information and notifies the rider through SMS or email. By automating helmet detection and penalty enforcement, this system enhances road safety, reduces manual intervention, and ensures adherence to traffic regulations.

关键词： Head Road accidents Manuals Machine learning Road safety Regulation Real-time systems Safety intelligent systems Monitoring

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Image Based Feature Separation Using RBM Tech with ADBN Tech for Accurate Fruit Classification 5

Image Based Feature Separation Using RBM Tech with ADBN Tech...

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5th IEEE international conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Revathi, V. Kavin, Balasubramanian Prabhu thirumalraj, Arunadevi Gangadevi, E. Balusamy, Balamurugan Gite, Shilpa New Horizon College of Engineering Department of Applied Sciences - Physics Ring Road Bellandur Post Bengaluru India School of Computing Srm Institute of Science and Technology Department of Data Science and Business Systems Chengalpattu Tamil Nadu Kattankulathur603 203 India K.Ramakrishnan College of Technology Department of Computer Science Engineering Tamil Nadu Trichy India Loyola College Department of Computer Science Chennai600034 India Delhi India Symbiosis Institute of Technology Pune Aiml Department Pune412115 India

ISBN: (纸本)9798350383522

One of the fastest-growing subfields of computer vision and picture categorization is fruit identification. the fruit industry could start using a standardised categorization system so that shoppers can easily distinguish between different types and quality levels of fruit. From a health perspective, fruit quality is essential. the task of classifying fruits is crucial in many commercial contexts. A supermarket cashier may use a fruit categorization system to better recognise the varieties of fruit and their associated costs. It might also be used to determine whether or not a given kind of fruit is suitable for a person's diet. the rising use of intelligent imaging equipment may be directly attributed to the proliferation and development of AI software. Deep learning methods, such as CNN, are now being used by researchers for picture categorization. CNN's superior performance may be achieved without using any manually-crafted characteristics, in contrast to the older methods. For automated categorization, it employs a plethora of filters that strip relevant visual information. Classifying fruits accurately is a challenge in the horticulture sector and calls for the knowledge of an expert. For accurately and quantitatively analysing fruits, the currently mentioned categorization techniques fall short. therefore, it is beneficial to review the latest suggestions for categorizing fruits. To solve this problem, we need a computerised system that can automatically sort fruits into their respective categories. Deep learning models, such as the Restricted Boltzmann Machine (RBM) and the Adam based Deep Belief Network (DBN), are used in this assignment to categorise fruits according to the attributes that have been determined to be optimum and generated. the proposed approach has been shown to have effective accuracy and quantitative analysis outcomes, and this is just the beginning of this realisation. In addition, the suggested scheme's relatively strong computational mo

关键词： Deep Belief Network Fruits Classification Fruits Grading Handcrafted features Restricted Boltzmann Machine

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PlantLDNet : Disease Detection in Leaf Images Using Deep learning Models

PlantLDNet : Disease Detection in Leaf Images Using Deep Lea...

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intelligent Communication Technologies and Virtual Mobile Networks (ICICV), international conference on

作者： Gurram Sai Nikhileswar Surya Sai Pampana Jani Anbarasi L Chintalapudi Likhitha Bhavana Rayanki Lakshmi Nanditha Kodela Chaithanya Krishna School of Computer Science and Engineering Vellore Institute of Technology Chennai

automated disease diagnosis in the agricultural field has benefited from the recent advances in big data, deep learning, and computer vision across a wide range of applications. However, intelligent disease prediction is crucial for safely maintaining a good agricultural production. Moreover, unclear disease prognosis is caused by the degree of leaf disease inhomogeneity, the complexity of the background-which includes the low contrast with the surrounding area and the potential for shadows with a similar intensity. this research provides a custom Convolutional Neural Network (CNN) model called PlantLDNet that has hyperparameters configured optimum to distinguish between leaf images showing disease and those without it, hence avoiding the problems associated with vanishing gradient. A comparative study of the proposed custom CNN model PlantLDNet and earlier research works was conducted using the following metrics: precision, recall, accuracy, F1 score, loss accuracy, receiver operating characteristic (ROC) curves, and Area Under Curve (AUC) measure. the comparison findings show that the proposed PlantLDNet model has the highest classification prediction effectiveness (99.85%) with less computational complexity. It also has an elevated high F1-score of 93% when trained for Early Bright, 98% for Healthy, and 92% for Late Bright.

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intelligent IoT System with AI and ML Integration for Smart Home Automation

Intelligent IoT System with AI and ML Integration for Smart ...

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Trends in Material Science and Inventive Materials (ICTMIM), international conference on

作者： P. Vishnu Kumar Allam Prathyusha Nowly HimaBindu Palukuru Manaswini Department of Internet of Things G. Pullaiah College of Engineering and Technology Kurnool Andhra Pradesh India

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

the integration of Artificial Intelligence (AI), Machine learning (ML), and the Internet of things (IoT) is transforming smart home automation by enhancing security, energy efficiency, and user convenience. this paper presents an AI-powered IoT system that employs real-time monitoring and adaptive automation to optimize household operations. the system utilizes ultrasonic sensors for water level monitoring, motion sensors for automated lighting and fan control, and DHT11 sensors for climate regulation. AI-driven facial recognition ensures secure access control, while predictive energy management reduces electricity consumption by dynamically adjusting appliance usage. the system effectively detects hazards such as gas leaks and fires in real time, triggering immediate alerts and safety measures. With seamless device coordination and automated responses, this framework enhances home security, energy efficiency, and overall automation reliability. Despite challenges in data security and system integration, the findings highlight that AI-ML-driven IoT automation is a viable solution for creating self-sustaining and intelligent smart homes.

关键词： Automation Face recognition Smart homes Machine learning Real-time systems Energy efficiency Sensor systems Hazards Internet of things intelligent sensors

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Comparing Ranking learning Algorithms for Information Retrieval Systems 24th

Comparing Ranking Learning Algorithms for Information Retri...

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24th international conference on intelligent data engineering and automated learning, ideal 2023

作者： Zilles, J. Borges, E.N. Lucca, G. Marco-Detchart, C. Berri, Rafael A. Dimuro, G.P. Centro de Ciências Computacionais Universidade Federal do Rio Grande Av. Itália km 08 Campus Carreiros Rio Grande96201-900 Brazil Programa de Pós-Graduação em Engenharia Eletrônica e Computação Universidade Católica de Pelotas Gonçalves Chaves Pelotas96015-560 Brazil Camino de Vera s/n Valencia46022 Spain

ISBN: (纸本)9783031482311

the growth of machine learning applications in various fields has enabled the advancement of Information Retrieval systems. As a result of this evolution, it has become possible to solve the well-known document classification problem. In the beginning, document positions within a result were given from a score, where each document receives an assigned value based on the terms used in the input query. the use of machine learning in this field is known as learning to Rank, which allows the classification of documents to better meet user search requirements, taking into account aspects such as document preference, importance, and relevance. this paper presents a comparison of different algorithms for ranking documents using machine learning. It is observed that RankSVM presents relatively satisfactory results in smaller datasets, while algorithms that use Gradient Boosting obtain better results for larger datasets. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Machine learning

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