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Paddy Leaf Disease Detection using Deep learning Methods 3

Paddy Leaf Disease Detection using Deep Learning Methods

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3rd international conference on intelligent Computing, Instrumentation and Control Technologies, ICICICT 2022

作者： Kishore, Indukuri Gowtham Phanindra Kumar, Kakaraparthi Vamsikrishna, Chetlapalli Dattasai Dilip Vignesh, Esarapu Reddy, Potham Raghavendra Nair, Aswathy K Amrita Vishwa Vidyapeetham Department of Electronics and Communication Engineering Amritapuri India

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

Computerised automated diagnosis of crops disease enables early detection and ensures the quality of crop. Technology advancements in these fields will reduce the loss and increase the overall productivity. Our research work motivated to build a deep learning classification model for paddy leaf disease detection. The model frame work consists of several pre-processing techniques such as denoising, data filtering, and selection of optimizer that best fits the model. Finally, a comparative study of the proposed model's performance and efficiency was done with different deep learning models. Based on the analysis and observation, it was observed that the proposed model has given promising results for effective leaf disease detection. © 2022 IEEE.

关键词： Crops

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Gradient Regularization with Multivariate Distribution of Previous Knowledge for Continual learning 23rd

Gradient Regularization with Multivariate Distribution of Pr...

引用

23rd international conference on intelligent data engineering and automated learning (ideal)

作者： Kim, Tae-Heon Moon, Hyung-Jun Cho, Sung-Bae Yonsei Univ Dept Comp Sci Seoul 03722 South Korea Yonsei Univ Dept Artificial Intelligence Seoul 03722 South Korea

ISBN: (纸本)9783031217524;9783031217531

Continual learning is a novel learning setup for an environment where data are introduced sequentially, and a model continually learns new tasks. However, the model forgets the learned knowledge as it learns new classes. There is an approach that keeps a few previous data, but this causes other problems such as overfitting and class imbalance. In this paper, we propose a method that retrains a network with generated representations from an estimated multivariate Gaussian distribution. The representations are the vectors coming from CNN that is trained using a gradient regularization to prevent a distribution shift, allowing the stored means and covariances to create realistic representations. The generated vectors contain every class seen so far, which helps preventing the forgetting. Our 6-fold cross-validation experiment shows that the proposed method outperforms the existing continual learning methods by 1.14%p and 4.60%p in CIFAR10 and CIFAR100, respectively. Moreover, we visualize the generated vectors using t-SNE to confirm the validity of multivariate Gaussian mixture to estimate the distribution of the data representations.

关键词： Continual learning Memory replay Sample generation Multivariate gaussian distribution Expectation-maximization

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Association Rules Mining for Reducing Items from Emotion Regulation Questionnaires 23rd

Association Rules Mining for Reducing Items from Emotion Reg...

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23rd international conference on intelligent data engineering and automated learning (ideal)

作者： Khadimallah, Rihab Kallel, Ilhem Drira, Fadoua REGIM Lab Res Grp Intelligent Machines Sfax Tunisia FSEGS Fac Econ & Management Sfax Sfax Tunisia ISIMS Higher Inst Comp Sci & Multimedia Sfax Sfax Tunisia ENIS Natl Sch Engn Sfax Sfax Tunisia

ISBN: (纸本)9783031217524;9783031217531

Regulating learners' negative emotions during a learning session is an important factor in educational settings that aims at enhancing learner's cognitive performance and achievement outcomes. In this context, many researchers in psychology have proposed emotion regulation questionnaires that help to assess the use of emotion regulation strategies by learners in order to regulate their emotions while learning. However, the number of items in each questionnaire is large which may annoy the learner and prevent him/her from completing all of the items;this may lead to inappropriate emotional regulation. Thus, we propose in this paper a machine learning method for mining items in order to reduce the fully associated ones to one item. First of all, the paper presents a critical overview on statistical methods applied for reducing the large number of items in questionnaires. Then, after the introduction of the selected data set about the emotion regulation questionnaires, we detail the association rules mining method and discuss the obtained results about the significant association rules between the items. This can lead to uphold the items' reduction without loss of reliability.

关键词： Emotion regulation questionnaire (ERQ) Reducing items Association rules mining

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IoT and Cloud based automated Irrigation System 3

IoT and Cloud based Automated Irrigation System

引用

3rd international conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Anandha Jothi, K.G. Vignesh, T.A. Gokul Nittin, A. Vinoth, M. Janani Shruthi, B. Abhishek, V. Srinivasan, Neeraj Govardhan, K. Vellore Institute of Technology School of Electronics Engineering Vellore India Vellore Institute of Technology VIT School of Agricultural Innovations and Advanced Learning Vellore India

ISBN: (纸本)9798350372120

Agriculture is the most important sector in our country, employing more than 50% of our population and always necessitating innovative solutions for resource utilization and enhanced productivity. In this novel agricultural project, we introduce a versatile and intelligent IoT-based irrigation system. By integrating soil moisture sensors, water level detectors, and weather prediction APIs, our system empowers farmers with real-time insights and automated control over irrigation processes. A key aspect of our strategy is incorporating cloud services, where all data collected by the sensors are sent and analyzed. With a user-friendly mobile app built on Flutter, users can easily supervise and manage the watering system from a distance. Additionally, by utilizing weather predictions obtained through API requests, the system automatically adjusts watering times, ensuring the best conditions for plant growth. One notable feature of our system is its ability to adjust to different types of crops. By employing artificial intelligence algorithms, the system intelligently determines the specific water needs for each crop chosen by the farmer. This personalized technique not only optimizes resource usage but also boosts crop *** summary, this project offers a practical solution for automatic crop irrigation by considering real-time weather forecasts. The proposed device improves irrigation performance and promotes climate-resistant cropping by combining climate monitoring, weather forecasting, and automatic pumping. © 2024 IEEE.

关键词： Crops

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Automobile Insurance Fraud Detection: An Overview 3

Automobile Insurance Fraud Detection: An Overview

引用

3rd international conference on intelligent engineering and Management, ICIEM 2022

作者： Kini, Ajay Chelluru, Rohit Naik, Kushang Naik, Dattdeep Aswale, Shailendra Shetgaonkar, Pratiksha SRIEIT Goa University Computer Engineering Shiroda India

ISBN: (数字)9781665467568

ISBN: (纸本)9781665467568

Frauds are committed in a highly professional way, due to which companies sometimes fail to identify that any fraud has occurred. Unprofessional frauds are also taking place, but they can be identified easily by the companies. Detecting a fraud traditionally is carried out using manual techniques, but by using data mining and algorithms from machine leaning or deep learning the detection process is automated and the frauds can be detected in a more efficient and structured way with more accuracy. The processing speed of information is gradually increased by using various algorithms of machine learning. The highlight of this survey paper is to compare different methods based on their performance. The algorithms help by detecting crucial patterns found in historical data and recognizing them if found in input data. The survey provides proper understanding of different methodologies used for automobile fraud detection. Sometimes machine learning cannot be used to detect multiple frauds involving behavior changes. We propose a vehicle insurance fraud detection mechanism using LSTM RNN networks. LSTM is commonly used in deep learning to design time series information. © 2022 IEEE.

关键词： Surveys

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FAS-reid: Fair Architecture Search for Person Re-IDentification 3

FAS-reid: Fair Architecture Search for Person Re-IDentificat...

引用

3rd IEEE international conference on Information Technology, Big data and Artificial Intelligence, ICIBA 2023

作者： Chen, Shengbo Chen, Shijie Lei, Zhou Shanghai University School of Computer Engineering and Science Shanghai200444 China Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai201112 China

ISBN: (纸本)9781665490788

The development of deep learning has driven the development of ReID, and more and more excellent methods have been proposed, but most of these are artificially designed network backbones. Automation is a trend in the development of deep learning. This paper propose FAS-ReID(Fair Architecture Search for Person Re-IDentification), which can automatically design and generate a neural network backbone for ReID. This paper compare operation selection to competition, and inject noise to make the competition fairer, while making the architecture of automated search More adaptable to ReID We use TriHard loss to improve the feature extraction ability. Our experiments show that the backbone searched by FAS-ReID is better and reduces the search time. Meanwhile, as for the problem of performance collapse caused by skip-connection enrichment in the search process, FAS-Reid does not use the early stop strategy to avoid performance loss like other solutions, which is also the reason why our method is more reliable and robust. © 2023 IEEE.

关键词： Automation

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A Deep learning Technique for Smart Gender Classification System 3

A Deep learning Technique for Smart Gender Classification Sy...

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3rd international conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Sonthi, Vijaya Krishna Sikka, Rishi Anusha, R. Devi, P. Bala Shanmuga Kamble, Sachin K Pant, Bhasker KoneruLakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram India Sanskriti University Department of Electronics Uttar Pradesh Mathura India A. P. C. Mahalaxmi College for Women English Thoothukudi India Terna Engineering College Design Navi Mumbai India Graphic Era Deemed to Be University CSE Uttarakhand Dehradun India

ISBN: (纸本)9798350399264

The examination of the possibility of employing deep learning techniques for transportation planning and modelling is the basis of this essay. The writers look at the transportation issues that have developed for urbanized populations. intelligent transportation systems will become increasingly crucial to satisfy people's transportation demands as the urban population grows. automated data gathering devices demand the usage of analytics tools in order to give insights for actual decision-making and policy planning. This research has two objectives. This research begins with a review of academic literature on gender and age identification using Convolutional Neural Network-based approaches estimate, followed by a thorough examination of the present methodology. Then, using the classification techniques, we conduct a review of roughly 30 publications to draw valid conclusions and forecasts about how the various CNN approaches and transportation systems will develop in the *** to the existence of a complicated background, object occlusion, and varying lighting conditions, gender determination from facial photos is a difficult process. Face photos may be used for a variety of purposes, including tracking, identification, and expression analysis. Two deep learning-based approaches are examined in this research for facial image-based gender classification. Convolutional neural networks (CNN) and Alex Net are two examples of these techniques. The effectiveness of both models for identifying the male and female classes from face photos was tested through experiments. Results indicate that both approaches were successful in classifying people by gender. Additionally, a comparison between these two models and a few of the common techniques for gender categorization was done. © 2023 IEEE.

关键词： Eye tracking

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A Convolutional Neural Network Approach for Optimal Steering Control of Self-Driving Cars 3

A Convolutional Neural Network Approach for Optimal Steering...

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3rd international conference on Image Processing and intelligent Control, IPIC 2023

作者： Hezam, Tameem Al mahbashi, Mohammed Almadwami, Hashem Almadwami, Ali School of Computer Science Nanjing University of Science and Technology 200 Xiaolingwei Street Jiangsu Nanjing210094 China School of Electronic and Control Engineering Chang'an University Shaanxi Xi'an710064 China School of Mechatronic Engineering Department of Mechanical Engineering Wuhan University of Technology 205 Luoshi Road Hongshan Dist. Hubei Wuhan430070 China School of Mechanical Engineering Nanjing University of Science and Technology 200 Xiaolingwei Street Jiangsu Nanjing210094 China

ISBN: (纸本)9781510668140

The end-to-end learning framework for autonomous cars has attracted considerable interest in recent years from both industry and academia. To control an autonomous car, this approach requires developing a model that links sensory perception, such as picture frames from a camcorder, to learn the optimal and safe driving actions. To develop a completely automated driving, it is possible to use professional driver experience more efficiently by using the end-to-end learning approach rather than spending a significant amount of money on labelling like bounding boxes for things. The conventional self-driving system, which typically consists of detection, positioning, mapping, and route planning manually fails to perform well with dynamic autonomous vehicles. We proposed a Convolutional Neural Network (CNN) framework for learning the autonomous driving to convert front-facing camera data from raw pixels and learn the steering commands. This end-to-end strategy has shown very promising results compared to the traditional self-driving models. The proposed CNN framework learns to intelligently drive in traffic on local roads with or without lane markings, on motorways, and with the least amount of training data from experts. Additionally, it also works smartly in places with obscure visual cues, including parking lots and gravel roadways. The system spontaneously picks up internal demonstrations of the essential processing phases, like identifying convenient road objects, by learning the human steering angle as the training input. © 2023 SPIE.

关键词： Autonomous vehicles

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Convolutional Neural Network Approach for Multiple Sclerosis Lesion Segmentation 23rd

Convolutional Neural Network Approach for Multiple Sclerosis...

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23rd international conference on intelligent data engineering and automated learning (ideal)

作者： Messaoud, Nada Haj Mansour, Asma Ayari, Rim Ben Abdallah, Asma Aissi, Mouna Frih, Mahbouba Bedoui, Mohamed Hedi Univ Monastir Lab Technol & Med Imaging Fac Med Monastir Tunisia Fatouma Bouguiba Hosp Dept Neurol Monastir Tunisia Univ Monastir Fac Sci Monastir FSM Monastir Tunisia

ISBN: (纸本)9783031217524;9783031217531

Nowadays Deep learning (DL) based automatic segmentation has outperformed traditionalmethods. In the present paper, we are interested in automatic MS lesion segmentation of 2D images based on DL techniques. The main challenge consists in proposing a new model that takes advantage of referenced CNN models: U-Net, ResNet, and DenseNet with a reduced number of parameters and a shorter execution time. To evaluate the proposed approach named "Concat-U-Net", we compared its performance to those of three implemented models, namely U-Net, U-ResNet, and Dense-U-Net. Furthermore, we employed just one modality (FLAIR) from the public ISBI dataset to segment MS lesions accurately. The best Dice value obtained was 0.73, which outperformed those reported in the literature. Our approach reduced the elapsed execution time from 48 s to 7 s. By reducing the number of parameters, an 85.42% time gain was achieved.

关键词： Deep learning Multiple sclerosis segmentation MRI data augmentation

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3rd international Informatics and Software engineering conference, IISEC 2022

3rd International Informatics and Software Engineering Confe...

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3rd international Informatics and Software engineering conference, IISEC 2022

ISBN: (纸本)9781665459952

The proceedings contain 80 papers. The topics discussed include: a secure and intelligent smart home controlling system;mobile platform for livestock monitoring and inspection;feature extraction and dimensionality reduction of cancer data using folded LDA;intrusion detection in computer networks using optimized machine learning algorithms;declarations of software engineering project managers managing remotely: provisions for hybrid working;vision transformers-based hand gesture classification;real-time automated license plate recognition and tracking of runaway vehicles;proposing an augmented reality-based system for fashion and beauty makeover;an augmented reality-based system for fashion and beauty makeover;and time series modeling and forecasting of expected monthly rainfall in some regions of northern Nigeria amid security challenges.

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