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检索条件"机构=Department of artificial intelligence and Machine Learning"

共 2191 条记录，以下是21-30 订阅

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Comparative Analysis of Algorithms for Cotton Plant Leaf Disease Classification from an Image 56

Comparative Analysis of Algorithms for Cotton Plant Leaf Dis...

引用

56th International Carnahan Conference on Security Technology, ICCST 2023

作者： Singh, Aniket Patra, Amitesh Tyagi, Arjun Wagle, Shivali Kamat, Pooja Department of Artificial Intelligence and Machine Learning Pune India

ISBN: (纸本)9798350315875

For disease diagnosis and yield optimization, the precise classification of pictures of cotton plant leaves into categories of healthy or diseased is a significant task in the agricultural sector. On a dataset of 2,310 photos that were made available to the public, we compared the performance of six categorization methods in this study. In terms of validation and test accuracy, our experimental results showed that ResNet50 performed better than other models, with ResNet50 reaching a validation accuracy of 0.980237 and a test accuracy of 0.9802. These results imply that ResNet is an excellent deep-learning model that can be used to accurately and dependably classify photos of cotton plant leaves in the agricultural sector. Our research demonstrates the capability of machine learning deep learning algorithms for disease diagnosis and yield improvement in the agriculture industry, which can ultimately lead to improved crop production and better food security. © 2023 IEEE.

关键词： Image classification

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2023 International Conference on artificial intelligence Science and Applications in Industry and Society, CAISAIS 2023

作者： Ghanem, Sahar I. Computer Science and Artificial Intelligence Faculty Artificial Intelligence and Machine Learning Department Egypt

ISBN: (纸本)9798350314786

In an attempt to facilitate the way students, succeed academically, academic advising is an integral part of the educational system. However, it has frequently been dependent on time-consuming manual processes and personalized interaction, which are not scalable. machine learning and deep learning have the potential to completely transform advising by automating parts of process and giving students distinctive guidance. This paper explores the relationship between student academic performance and correlated features using deep learning methodology in academic advising systems. The model was trained and tested with the Kaggle dataset, and the results show high accuracy, specificity, and true positive rate values compared to naive bayes and random forest methodologies. The paper also addresses potential challenges and limitations of deep learning in academic advising, such as data privacy concerns, algorithmic bias, and the need for human intervention. It concludes by emphasizing the importance of incorporating ethical considerations and human expertise in the development and deployment of deep learning models in academic advising. Overall, the paper highlights the potential of deep learning algorithms to transform academic advising and improve student success. © 2023 IEEE.

关键词： Students

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Predicting Urban Land Cover Using Classification: A machine learning Approach 11

Predicting Urban Land Cover Using Classification: A Machine ...

引用

11th IEEE Region 10 Humanitarian Technology Conference, R10-HTC 2023

作者： Jadhav, Tanush Tanishq Jagadish, Spoorthi Gaikwad, Mayur Wagle, Shivali Jayaswal, Ruchi Patil, Shruti Department of Artificial Intelligence and Machine Learning Pune India

ISBN: (纸本)9798350326147

The classification of urban land cover is a crucial step in comprehending the evolution of the urban environment and its effects. We are providing a comparative analysis of machine learning methods for classifying urban land cover using remote sensing data in this research. The study utilises the Urban Land Cover dataset which is obtained from the UCI machine learning Repository, which contains high-resolution images taken of urban areas. Also, comparison of several popular machine learning classification algorithms, such as Decision Tree classifier, Random Forest classifier, Support Vector machine classifier (SVM/SVC), XGBoost classifier, K-Nearest Neighbors classifier (KNN) and Ridge classifier is done along with their accuracy scores. This comparison shows that the Random Forest algorithm outperforms the other machine learning algorithms with an overall accuracy of 91.38 % after removing the outliers and using Grid Search CV to tune the hyper-parameters. © 2023 IEEE.

关键词： Classification (of information)

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The Smart Factory of Tomorrow: artificial intelligence and machine learning Reshaping Manufacturing Processes 2

The Smart Factory of Tomorrow: Artificial Intelligence and M...

引用

2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Priyanga, P. Ashwini, K. Deepa, S.R. Sridevi, S. Department of Artificial Intelligence and Machine Learning RNS Institute of Technology Karnataka Bangalore India Department of Computer Science and Design K.S. Institute of Technology Karnataka Bangalore India Tamil Nadu Chennai India

ISBN: (纸本)9798350305418

The smart factory of the future would not be possible without the development of AI and ML technologies, which have ushered in a new era of production. Traditional industrial processes are being revolutionized by AI and ML due to their capacity to evaluate large quantities of data and make autonomous choices, which is leading to greater efficiency, productivity, and profitability. Predictive maintenance is one area where AI and ML are making important contributions. These systems may prevent unexpected and expensive failures by constantly monitoring equipment performance and analyzing real-time data. Taking preventative measures like these results in less downtime, lower maintenance expenses, and more efficient machinery. Synergies between AI and ML are improving factory quality assurance. These technologies can identify even the smallest flaws or deviations from product standards using sophisticated vision systems and pattern recognition algorithms. Manufacturing companies may reduce waste and customer complaints by maintaining a constant quality standard via the use of automated inspection methods. Optimization of production planning and scheduling is another important use of AI and ML in manufacturing. © 2023 IEEE.

关键词： artificial intelligence Collaborative Robots (Cobots) Industrial Internet of Things (IIoT) Lean Manufacturing machine learning Manufacturing Processes Predictive Maintenance Production Planning Quality Control Smart Factory

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Developing an AI-Based Library Assistant: Enhancing Book Retrieval with Natural Language Processing and machine learning 2

Developing an AI-Based Library Assistant: Enhancing Book Ret...

引用

2nd International Conference on Computational intelligence, Communication Technology and Networking, CICTN 2025

作者： Kailash Varma, Nadimpalli Madana Aryan, Adla Dhanush, Pagilla Manikanta, Rangisetti Chandhu, Nalla Arora, Gagan Deep Department of Artificial Intelligence & Machine Learning Vardhaman College of Engineering Telangana Hyderabad501218 India

ISBN: (纸本)9798331530389

The current college library system is hindered by inefficiencies in book searching, availability updates, identity verification, and checkout processes, resulting in delays and errors that negatively impact student experiences. This research paper presents an Al-based librarian system designed to address these challenges through automation and digital transformation. Leveraging a Large Language Model (LLM), the proposed system facilitates personalized interactions between students and library resources. Upon student verification, the AI librarian provides real-time information on book availability, location, and tailored recommendations, significantly reducing the time spent on manual searches. The streamlined checkout process allows for automated book issuance and instant confirmation notifications, minimizing human error and enhancing record- keeping. This innovative solution not only improves operational efficiency but also enriches the user experience by offering a user-friendly interface and timely assistance. Future enhancements, such as voice integration and mobile application support, are suggested to further modernize library services. This research underscores the potential of AI technologies to revolutionize library management and improve service delivery in academic settings. © 2025 IEEE.

关键词： Efficiency

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Enhanced Edge Attention-Based SRCNN for Retinal Image Super-Resolution 1

Enhanced Edge Attention-Based SRCNN for Retinal Image Super-...

引用

1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Koshika, Manoj Kandukuri, Prabhakar Moppey, Shyam Kumar Chaitanya Bharathi Institute of Technology Department of Artificial Intelligence & Machine Learning Gandipet Hyderabad India

ISBN: (纸本)9798331529246

The early recognition of retinal disorders such as cataracts, glaucoma, and retinal problems is highly dependent on high-resolution retinal imaging, which is a complex issue because of poor image quality and the lack of important features. The proposed Enhanced Edge Attention Super Resolution Convolutional Neural Network (EEASRCNN) is designed to improve the quality of retinal images high precisely. The model preserves important details like blood vessels and the optic disc by using a custom edge loss function, sharpening filters, and combined attention mechanisms. With a high structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR) score and an accuracy of 97.16% in preserving structural details, EEASRCNN outperforms traditional super-resolution methods after being trained on a dataset of diverse retinal images. EEASRCNN offers effective image super resolution in real-time medical image diagnosis. © 2025 IEEE.

关键词： Ophthalmology

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ProductGuard: Empowering Informed Choices for Safe Living 3

ProductGuard: Empowering Informed Choices for Safe Living

引用

3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Vadlamudi, Sandyarani Shashikala, K.S. Patil, Suyash Ganesh Shetty, Rajath U. Department of Artificial Intelligence & Machine Learning New Horizon College of Engineering Karnataka Bangalore India

ISBN: (纸本)9798331527549

In a landscape where consumer decisions significantly influence personal health and well being, the necessity for informed choices regarding product safety is paramount. This project introduces ProductGuard, a mobile application designed to empower users in making conscientious and informed decisions about skincare and personal care products. The project integrates four core components aimed at assisting users in evaluating product safety, comparing ingredients, providing personalized recommendations based on skin type, and offering expert guidance through a chatbot interface. The first component employs advanced image analysis technology utilizing a dataset extracted from the Environmental Working Group (EWG) website, enabling users to capture product ingredient images and receive detailed safety analyses. The second component allows comparative analysis by uploading images of two product ingredient lists, swiftly determining the safer option. Leveraging deep learning models and MobileNetV2, the third component performs skin type classification based on facial images, ensuring accurate categorization into dry, oily, normal, or sensitive skin, further providing tailored product recommendations. Lastly, a chatbot powered by ChatGPT serves as the fourth component, furnishing users with real-time guidance on skincare, ingredient queries, and safe living practices. Developed using React Native, Flask integration, and Python backend, this application amalgamates cutting-edge technology with user-centric design for seamless navigation and accessibility across various devices. ProductGuard surpasses existing systems by offering a holistic approach to skincare analysis, integrating advanced image recognition, personalized recommendations, and real-time expert assistance, thereby empowering users to make informed and safer skincare choices. © 2025 IEEE.

关键词： Bottles

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Enhancing Pneumonia Treatment in Children by machine learning Techniques 4

Enhancing Pneumonia Treatment in Children by Machine Learnin...

引用

4th International Conference on Sentiment Analysis and Deep learning, ICSADL 2025

作者： Gajbhiye, Ashwini Gundewar, Swapnil Verma, Prateek Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha442001 India

ISBN: (纸本)9798331523923

Pneumonia is a health challenge that affects many people more so the children, thus leading to high death rates across the world. There is a persuading evidential proof which shows that the techniques in the application of the machine learning (ML) have helped in improving the diagnosis and treatment of pneumonia. This paper discusses a number of ML techniques such as CNNs, SVMs and Random Forest that can be used to enhance the reliability and effectiveness of detecting pneumonia from enhanced chest x-ray images. The classification shows how other methods like Min-Max normalization and a specific type of image pre-processing called Contrast-Limited Adaptive Histogram Equalization (CLAHE) can make image quality even better as well as increase the accuracy of the model. Here, by using the sophisticated dalliance of ML models it is possible to dramatically minimize the incidence of diagnostic errors and enhance the results of pneumonia treatments thus witnessing an important evolutionary leap in the specialty. © 2025 IEEE.

关键词： Convolutional neural networks

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Visionary Cardioscan: Heart Risk Detection through Retinal Imaging 3

Visionary Cardioscan: Heart Risk Detection through Retinal I...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Arun Kumar, S. Dharshini, B. Hemanth, M. Vignesh, S. Saratha, M. Department of Artificial Intelligence and Machine Learning M.Kumarasamy College of Engineering Karur India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527549

Heart attacks and hypertension are two cardiovascular disease risk factors that have a major impact on the microvascular system's structure and function. Fundus camera images can detect abnormalities in retinal blood vessels that indicate vascular injury. Using machine learning techniques, preclinical signs of damage, which may not be visible to an observer, can be detected. It proposes a system that uses retinal images to predict heart attack risk. The model leverages vessel segmentation to extract morphological details of retinal blood vessels. The results can assist in identifying heart disease early, especially in young patients. © 2025 IEEE.

关键词： Ophthalmology

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BACE1 Candidates Inhibitors for Alzheimer's Drug Discovery Using the Transformer-Only Decoder 6

BACE1 Candidates Inhibitors for Alzheimer's Drug Discovery U...

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6th IEEE Novel Intelligent and Leading Emerging Sciences Conference, NILES 2024

作者： Elhashmy, Mostafa Abdelaziz Hamdalla, Mostafa El Rashidy, Nora Faculty of Artificial Intelligence Kaferelshikh University Machine Learning and Information Retrieval Department Egypt

ISBN: (纸本)9798350378511

Our research introduces a groundbreaking approach for creating new molecular structures targeting Beta-secretase 1 (BACE1) in Alzheimer's disease treatment. By using a deep learning framework with a pre-trained Transformer model, fine-tuned on a Selfies BACE1 dataset, we enable accurate and meaningful generation of potential therapeutic compounds. This innovative approach streamlines drug discovery and offers a scalable solution for addressing various therapeutic targets. © 2024 IEEE.

关键词： Drug discovery

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