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检索条件"任意字段=2023 International Conference on Computer Engineering and Distance Learning, CEDL 2023"

共 40757 条记录，以下是4301-4310 订阅

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Deep learning Approach in Cotton Plant Disease Detection: A Survey 2

Deep Learning Approach in Cotton Plant Disease Detection: A ...

引用

2nd international conference for Innovation in Technology, INOCON 2023

作者： Bera, Soumyajit Kumar, Puneet Chandigarh University Department of Computer Science and Engineering Mohali India

ISBN: (纸本)9798350320923

The economy of our nation is rooted in agriculture,, which also has a significant impact on how we live our everyday lives. Nevertheless, cotton plant diseases anthracnose and crown gall led to by organisms that cause illness, including fungi as well as bacteria have cut back on agricultural production *** illnesses can be managed if identified and cured at an appropriate suitable ***, it seems to be quite challenging to manually detect these illnesses from the onset over a large area of *** most of the often when it is performed manually, these abnormalities really aren't discovered, and no suitable measures are made to remedy the *** today 's society, everything is intelligent and scientifically reliant. Machine learning as well as artificial intelligence (AI) are crucial to the development of intelligent *** consequence is the introduction of a quick and clever recommended method for diagnosing agricultural *** study emphasizes on the application of CNN modeling to identify and categorise illnesses affecting cotton plants. © 2023 IEEE.

关键词： Deep learning

来源：评论

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Leveraging Artificial Intelligence and Machine learning for Enhanced Crop Yield: A Crop Recommendation System for Indian Agriculture

Leveraging Artificial Intelligence and Machine Learning for ...

引用

2023 international conference on IoT, Communication and Automation Technology, ICICAT 2023

作者： Mishra, Saksham Gupta, Manish Buddha Institute of Technology Gida Department of Computer Science and Engineering Uttar Pradesh Gorakhpur India

ISBN: (纸本)9798350302820

This paper introduces a all-inclusive crop recommendation system for Indian agriculture, leveraging artificial intelligence and machine learning to increase crop yield and its productivity. Indian agriculture faces challenges from rapid population growth, urbanization, and climate change, necessitating innovative solutions to maintain food security. The proposed system combines AI and ML techniques with domain-specific knowledge to provide farmers with accurate, well timed and personalized recommendations on suitable crops, taking into account factors like soil type, climatic conditions, and market demand. By optimizing crop management through data-driven insights, this system aims to revolutionize traditional agricultural practices, ultimately leading to increased productivity, profitability, and viable farming in India. © 2023 IEEE.

关键词： Crops

来源：评论

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Implementing Deep learning Techniques into Healthcare System Improvement 2

Implementing Deep Learning Techniques into Healthcare System...

引用

2nd international conference on Electronics and Renewable Systems, ICEARS 2023

作者： Kochhar, Harmeet Kaur Kilaru, Mrudula Aljohani, Nasser Goyal, Himanshu Rai Singh, Devendra Saini, DilipKumar Jang Bahadur Chandigarh University University Institute of Media Studies India Velagapudi Ramakrishna Siddhartha Engineering College India Islamic University of Madinah Information Systems Department Madinah Saudi Arabia Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttarakhand Dehradun248002 India Uttaranchal University Uttaranchal Institute of Technology Uttarakhand Dehradun India Sawmi Rama Himalayan University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350346640

Artificial intelligence (AI) has the potential to transform clinical decision-making and assistance. In any case, the utilization of current AI methods remains only as a supportive solution to make clinical decisions. The existing methodologies fail to perform in-depth analysis to deliver accurate predictions. This study reviews the integration of advanced technologies AutoML in healthcare sector by considering the data privacy, heterogeneity and interpretability. This review will be a useful resource for medical practitioners interested in applying information science methods to their field of study. © 2023 IEEE.

关键词： Data privacy

来源：评论

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A Comparative Analysis of ResNet and MobileNet for Classifying MRI Images 5th

A Comparative Analysis of ResNet and MobileNet for Classifyi...

引用

5th international conference on Data Science, Machine learning and Applications

作者： Padmaja, D. Lakshmi Nikhil, B. Akshaya, Banda Sai Deepak, G. Surya Anurag Univ Hyderabad India North Carolina A&T State Univ Greensboro NC USA Cognizant Technol Solut Chennai India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

This work represents a comprehensive analysis of the performance of two popular deep learning architectures, ResNet and MobileNet, with particular attention to their use in the classification of magnetic resonance imaging (MRI) pictures. Healthcare professionals need to accurately classify medical images in order to make precise diagnosis and develop successful treatment plans. In this paper authors have done thorough comparative research to clarify the quantitative performance indicators while also exploring qualitative elements, such as the subtle differences between each model's strengths and weaknesses. Beyond the technical assessment, the study investigates ResNet's and MobileNet's computational effectiveness and flexibility in response to the various features present in medical imaging data. The project aims to provide a sophisticated understanding of these deep learning systems in order to make a significant addition to the medical image analysis field as a whole. The ultimate goal is to promote improvements in diagnostic accuracy, which will enable healthcare providers to make better judgments and provide better patient care. The results of this study will be crucial in determining the direction of future advancements in this important field as deep learning and medical imaging continue to cross paths. They provide insightful information that goes beyond ResNet and MobileNet to affect the larger field of deep learning applications in medical diagnostics and treatment planning. The aforementioned study highlights the profound potential of deep learning technology to enhance healthcare procedures and further advance medical science.

关键词： ResNet MobileNet Advancements Deep learning Algorithms computer Vision

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Deep learning Character Recognition of Handwritten Devanagari Script: A Complete Survey 1

Deep Learning Character Recognition of Handwritten Devanagar...

引用

1st IEEE international conference on Contemporary Computing and Communications, InC4 2023

作者： Sharma, Arpit Mithun, B.N. Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350335774

Recognition of handwritten characters is a concept in which the single characters are classified, it is a facility of an electronic device to scan and decipher the handwritten input from a variety of sources, including written texts, images, and other digital touch-screen devices. This concept is being used in distinctive sectors such as the processing of bank checks, form data entry, and parcel posting and nowadays it is becoming a very important issue in the pattern recognition domain and a very challenging task to resolve it. Since deep learning is a crucial strategy in solving detection and pattern recognition problems, several algorithms are available to classify the characters with better prediction rates on different datasets, and ultimately, whichever algorithm gives the optimized results will be considered the best solution for the character recognition problem. As a result, various solutions proposed by the existing researchers are discussed using deep learning algorithms in this survey article. © 2023 IEEE.

关键词： Character recognition

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An Association Based Approach to Elicit and Measure Impact of Features on Sales of a Garment Retail

An Association Based Approach to Elicit and Measure Impact o...

引用

2023 international conference on Artificial Intelligence and Applications, ICAIA 2023 and Alliance Technology conference, ATCON-1 2023

作者： Rout, Dillip Kotangale, Archana Nath, Sayantan Roy, Bholanath Sandip University School of Computer Science and Engineering Department of Computer Science and Engineering Nashik India Mumbai University A P Shah Institute of Technology Department of Computer Science and Engineering Thane India Maulana Azad National Institute of Technology Department of Computer Science and Engineering Bhopal India

ISBN: (纸本)9781665456272

In this article, an association-based approach is proposed for determining the feature importance of a given dataset which includes the target variable. In particular, the concept of Market Basket Analysis (MBA) is applied for enumerating the relationships between the target variable and each of the features which lead to the importance of those. Mention that the MBA is generally used for obtaining the recommended items based on the togetherness of the items. Nevertheless, an attempt is made in this paper to correlate the features given a target output by abstracting each feature to be paired with the target variable. The apriori algorithm and association rules are used for accounting for the coupling of features with the target feature. Precisely, Lift metric of MBA is the key to computing the associativity in this context. That is, each feature's importance is the sum of the individual ratio of Lift count of its values (observations) when paired with the target feature. The proposed methodology is tested on a dataset of a garment retail store that has information on several dresses. Each dress contains fifteen features including sales which is the lone numerical feature amidst the categorical features. Note that the sales are influenced by some of the features which generally the customers look for to prefer a particular dress over others. The results of the proposed methodology suggest that a couple of features are instigating sales at a higher rate than others. The outcome of the developed methodology is able to define a clear grouping of features according to the importance related to the target variable. The proposed methodology is applicable to a dataset where the feature selection is with respect to a target feature which is generally done in the case of supervised learning. © 2023 IEEE.

关键词： Sales

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Analytical Approach of Machine learning for Prediction Models 6

Analytical Approach of Machine Learning for Prediction Model...

引用

6th international conference on Contemporary Computing and Informatics, IC3I 2023

作者： Kumar, Mohit Dubey, Sanjay Kumar Amity School of Engineering and Technology Department of Computer Science and Engineering Amity University Uttar Pradesh Noida India

ISBN: (纸本)9798350304480

With the increasing availability of data, machine learning (ML) predictive models have become a popular tool for making informed decisions in various fields. However, choosing suitable algorithms and techniques to develop accurate forecasting models is a challenging task. In this study, we aim to compare different ML algorithms and methods to develop accurate prediction models. We analyzed the results to determine the most effective approach to developing accurate forecasting models. In addition, we investigated the effect of hyperparameter tuning on the performance of this model. The results of this study can provide valuable insights for practitioners and researchers in choosing the most appropriate machine learning algorithms and methods to develop accurate prediction models. Our study shows the impact of choosing the right features and choosing the method of data pre-processing and hyperparameter tuning on model performance. Finally, our results can inform the design of more accurate prediction models and contribute to the development of the field of machine learning. © 2023 IEEE.

关键词： decision-tree machine learning neural network regression

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Application of Machine learning Algorithms in Medical Image Analysis: A case study for Breast Cancer detection 10

Application of Machine Learning Algorithms in Medical Image ...

引用

10th international conference on Signal Processing and Integrated Networks, SPIN 2023

作者： Tripathi, Aashi Anwar, Shamama Birla Institute of Technology Mesra Department of Computer Science and Engineering Ranchi835215 India

ISBN: (纸本)9781665490993

Image processing is a very vital part of many medical diagnosis. With the advent of more technologically advanced devices, machine learning implementation has also proven to be boon in the medical world for imaging related diagnosis. This paper aims to utilize different machine learning models to highlight its efficacy in the field of medical image analysis. The paper uses machine learning classifiers to classify breast cancer into malignant and benign using 31 attributes of the Wisconsin Breast Cancer Diagnostic dataset. Five different classifier models - Decision Trees, SVM, Naive Bayes, KNN and ANN were used to classify the tumors and it was observed and concluded that SVM model performed better with an accuracy of approximately 97.4% followed by ANN model with an accuracy of approximately 96.5%. © 2023 IEEE.

关键词： Medical imaging

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Plant Leaf Disease Detection: A Deep Hybrid learning Approach

Plant Leaf Disease Detection: A Deep Hybrid Learning Approac...

引用

2023 IEEE international conference on Modelling, Simulation and Intelligent Computing, MoSICom 2023

作者： Kumar, Santosh Singh, Shivam National Institute of Technology Department of Computer Engineering Kurukshetra136119 India

ISBN: (纸本)9798350393415

Recent breakthroughs in Deep learning have led to a shift in the classification methods for plant infections towards automated feature detection. In this work, we evaluate the performance of existing algorithms for identifying plant leaf diseases on a particular dataset. In addition, we aim to identify the most efficient methods for detecting and classifying plant leaf diseases. First, we employed state-of-the-art techniques such as VGG16, VGG19, and RESNET50. Due to the small number of members per class in the data set, these techniques could not produce satisfactory results. On the other hand, traditional machine learning procedures require an expert to identify and validate feature extraction approaches. Consequently, our goal has been to design a hybrid model combining both advantages. We envisioned developing a method for detecting plant leaf diseases. © 2023 IEEE.

关键词： Convolutional neural networks

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MOVING FORWARD BY MOVING BACKWARD: EMBEDDING ACTION IMPACT OVER ACTION SEMANTICS 11

MOVING FORWARD BY MOVING BACKWARD: EMBEDDING ACTION IMPACT O...

引用

11th international conference on learning Representations, ICLR 2023

作者： Zeng, Kuo-Hao Weihs, Luca Mottaghi, Roozbeh Farhadi, Ali Paul G. Allen School of Computer Science & Engineering University of Washington United States PRIOR @ Allen Institute for AI United States

A common assumption when training embodied agents is that the impact of taking an action is stable;for instance, executing the "move ahead" action will always move the agent forward by a fixed distance, perhaps with some small amount of actuator-induced noise. This assumption is limiting;an agent may encounter settings that dramatically alter the impact of actions: a move ahead action on a wet floor may send the agent twice as far as it expects and using the same action with a broken wheel might transform the expected translation into a rotation. Instead of relying that the impact of an action stably reflects its pre-defined semantic meaning, we propose to model the impact of actions on-the-fly using latent embeddings. By combining these latent action embeddings with a novel, transformer-based, policy head, we design an Action Adaptive Policy (AAP). We evaluate our AAP on two challenging visual navigation tasks in the AI2-THOR and Habitat environments and show that our AAP is highly performant even when faced, at inference-time with missing actions and, previously unseen, perturbed action space. Moreover, we observe significant improvement in robustness against these actions when evaluating in real-world scenarios. © 2023 11th international conference on learning Representations, ICLR 2023. All rights reserved.

关键词： Embeddings

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