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共 25762 条记录，以下是211-220 订阅

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Harnessing Machine Learning for Accurate Corn Leaf Disease Classification 1

Harnessing Machine Learning for Accurate Corn Leaf Disease C...

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1st International Conference on Trends in Engineering Systems and Technologies, ICTEST 2024

作者： Pavithra, Lagisetty Naga Reddy, Hithaishi Bhargavi, M.S. Bangalore Institute of Technology Dept. of Computer Science and Engg. Bengaluru India

ISBN: (纸本)9798350307795

Corn, a vital global agricultural commodity, confronts significant threats from diseases that adversely impact yield and quality. Ensuring its stable and sustainable production is important for global food security, farmer prosperity, and national economic stability. Given the diverse challenges faced by corn cultivation, this research investigates the application of various machine learning models in the context of corn disease classification. This research provides an understanding of how various machine learning models react to various feature extraction techniques. The study compares the performance of the Histogram of Oriented Gradient (HOG), Discrete Wavelet Transform (DWT), and ResNet50 for feature extraction. The methodology encompasses image collection, preprocessing, feature extraction, and classification employing machine learning algorithms. Comparative analyses involving ResNet50, HOG, and DWT feature extraction techniques revealed that the ResNet50-based model achieved the highest accuracy. Furthermore, the results underscore the superior performance of the Support Vector Classifier (SVC) when coupled with ResNet50, highlighting its suitability for corn disease classification. This methodology not only offers a robust solution for efficient corn disease diagnosis but also contributes significantly to the advancement of agricultural disease management. © 2024 IEEE.

关键词： Discrete wavelet transforms

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Optimal Deep Learning Models for Post-Flood House Detection in Drone Imagery

Optimal Deep Learning Models for Post-Flood House Detection ...

引用

2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Jain, Swati Sushmitha, M. Einstein, A Manish Mahapatra, Ansuman National Institute of Technology Puducherry Dept. of Computer Science and Engineering Karaikal India

ISBN: (纸本)9798331540685

Floods remain a persistent threat to human lives and infrastructure. Identifying flood-impacted houses in drone imageries will support the rescue teams to prioritize rescue operations in highly affected areas. This study investigates the performance of various deep-learning detection models for post-flood house detection using drone imagery. Our research aims to identify the most suitable model and optimized parameters for accurately and efficiently identifying flood-affected houses, thereby facilitating timely disaster response and recovery efforts. The paper explores various state-of-the-art architectures and provides a thorough performance evaluation. © 2024 IEEE.

关键词： Flood damage

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Binary Classification of Breast Tumours using CBAM-Enhanced Collaborative Network 3

Binary Classification of Breast Tumours using CBAM-Enhanced ...

引用

3rd International Conference on Advancement in Electrical and Electronic Engineering, ICAEEE 2024

作者： Das, Soumit Nivel, Mst Fabia Akter Rajshahi University of Engineering & Technology Dept. of Computer Science and Engineering Rajshahi Bangladesh

ISBN: (纸本)9798350388282

For women breast cancer is a life-threatening problem, making the importance of early detection crucial. Expert pathologists assess cell shapes in breast histopathology images at various magnification levels for accurate diagnosis. Unfortunately, a shortage of pathologists in many countries heightens the risk of human error in disease identification and treatment. To address this challenge, this study proposed a CBAM-Enhanced Collaborative Network for classifying benign and malignant tumors in breast cancer histopathological images, offering a potential solution to enhance accuracy and efficiency in diagnosis. The model included a transfer learning branch using a pretrained DenseNet121 structure with integrated CBAM (Convolutional Block Attention Module). Simultaneously, a collaborative branch employed Inception-ResNet-v2 with CBAM for feature extraction from histopathological images. The feature fusion module then optimized information from both branches. The proposed method achieved impressive binary classification accuracies on the BreaKHis dataset: 98.31% overall, with 98.33%, 98.08%, 99.67%, and 97.80% for magnification factors of 40x, 100x, 200x, and 400x, respectively. CBAM-Enhanced Collaborative Network also exhibits strong generalization capabilities, as evidenced by its superior performance on the ICIAR2018 BACH Challenge dataset. © 2024 IEEE.

关键词： Image classification

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Leveraging Language Models for Human Activity Recognition for Intelligent Lighting

Leveraging Language Models for Human Activity Recognition fo...

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2024 IEEE Sustainable Smart Lighting World Conference and Expo, LS24 2024

作者： Mahmoudi, Ali Ozcelebi, Tanir Meratnia, Nirvana Eindhoven University of Technology Math. and Computer Science Dept. Eindhoven Netherlands

ISBN: (纸本)9798331540197

Recognizing human activities is necessary to achieve fully context-aware lighting in a smart home. Ambient sensors suit this task as they are ubiquitous, non-intrusive, and non-obstructive. However, each home has a unique floor plan and a unique number and placement of these sensors. This leads to heterogeneity of input data spaces for machine learning models, which hinders their use in different homes. We propose that converting the data into the semantic input space of a language model can not only unify the input space across different houses but also improve the prediction performance thanks to the model's understanding of semantics. We compared a pre-trained BERT model with one trained from scratch to test the proposed semantic conversion on three datasets from CASAS. The results show that the pre-trained model performs better on accuracy and F1-score. © 2024 IEEE.

关键词： Semantics

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A Novel Approach for Single-Shot Target Recognition by Matching Graph of Feature Pixels 2

A Novel Approach for Single-Shot Target Recognition by Match...

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2nd International Conference on Machine Learning and Data Engineering, ICMLDE 2023

作者： Mitra, Baldivya Deshmukh, Maroti Kumar, Abhimanyu Dept. of Computer Science & Engineering National Institute of Technology Uttarakhand 246174 India

Image feature extraction and matching constitute fundamental steps in computer vision and image analysis, enabling various applications ranging from object recognition to scene reconstruction. Object recognition is one of the major applications of artificial intelligence. Since last two decades, different techniques for object recognition were given by researchers from across the world. This paper presents a novel approach for automatic target recognition in autonomous weapons by matching connected graph of feature pixels. The Moving and Stationary Target Acquisition and Recognition (MSTAR) is taken for experimental purpose. The Proposed model has three stages for a target recognition;Image Denoising, Feature extraction & graph generation, and feature matching. Firstly, a sample image of targets taken from the MSTAR is taken as an input. Further, input sample is de-noised and features are extracted using Speeded-Up Robust Features (SURF) algorithm. Thereafter, a graph is created using feature pixels and this complete process is repeated with test image. Further, both graphs are compared using a proposed feature graph matching algorithm which results an recognition accuracy score. Proposed model is tested over the MSTAR dataset and it resulted in varying accuracy at different feature matching thresholds. © 2024 Elsevier B.V.. All rights reserved.

关键词： Synthetic aperture radar

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Novel Approach to Monitor Location of the Student in the Institution 15

Novel Approach to Monitor Location of the Student in the Ins...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Kulkarni, R.N. Suma, Rochana Y. Sravanthi, B. Rakshitha, G. Krathika, T. Ballari Institute of Technology and Management Dept. of Computer Science & Engineering Ballari India

ISBN: (纸本)9798331300579

During the last few decades there is a tremendous growth in the information and communication technology wherein many organizations are making use of the hardware and software for various applications. Marking the attendance of a student in the school or college as well as the presence of the employee in the workplace is to be either captured or manually marked in the registers. Performing various analyses by taking the manual input is cumbersome because the number of students in the class or number of employees in the organizations are increasing. To overcome the difficulties of managing the things manually in this paper we are proposing an automated tool to capture the presence of the student in the specific location. This paper proposes a novel approach to mark the presence of a student in the college premises. The tool developed here is an Android Application (App) wherein the students can send the details to the App. Two modules are proposed here, one for the student and another for the Administrator. Through the student module, each student can login to the App and capture the self-image and send it to the Administrator Module. The captured image comprises the GPS details like Longitude, Latitude, Time, and date information. The information sent through the App determines the location of the student whether the student is in the college premise such as Class room, Laboratory and Library. The application which is managed by the teacher or the administrator or proctor will get the details about the student's image, GPS locations such as latitude, longitude, date and time. The mobile app verifies the GPS location and image of the student and compares it with the database. If the captured information matches with the appropriate GPS location then the students attendance is marked as present in that particular duration and appropriate files are updated. © Grenze Scientific Society, 2024.

关键词： Face recognition

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Flood Location, Management and Solution(FLMS): A Flood Prediction and Management System for Kurla 3

Flood Location, Management and Solution(FLMS): A Flood Predi...

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3rd International Conference on Advanced Computing Technologies and Applications, ICACTA 2023

作者： Naidu, Siddhanth Pandey, Ambuj Patel, Shiab Wagaskar, Kalpita Tripathy, Amiya Kumar Sen, Sumit Don Bosco Institute of Technology Dept. of Computer Engineering Mumbai India Indian Institute of Technology Bombay Dept. of Computer Engineering Mumbai India

ISBN: (纸本)9798350348347

Flooding is a common problem in Kurla, a suburban area of Mumbai, India, with significant impacts on infrastructure, public health, and their livelihoods. This paper proposes FLMS, a Flood Location, Management, and Solution system designed to predict flood events in Kurla to assist in managing them. The FLMS system uses historical data on rainfall gathered from different sources, water levels, and other factors to develop a prediction model based on machine learning techniques. FLMS being a web based application will allow its users to view alerts, contact the respective personnel and create awareness about the different stages of a disaster and ways to limit the losses caused by floods. © 2023 IEEE.

关键词： Machine learning

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Detecting Deceptive URLs: Exploring Logistic Regression and Decision Tree Models

Detecting Deceptive URLs: Exploring Logistic Regression and ...

引用

2024 IEEE Students Conference on Engineering and Systems, SCES 2024

作者： Kumar, Bhavishya Maria Anu, V. Vellore Institute of Technology Dept. of Computer Science&Engg. Chennai India

ISBN: (纸本)9798350374711

This research investigates the detection of phishing websites utilizing machine learning techniques and feature selection methodologies. Phishing remains a prevalent cyber threat, posing significant risks to users' sensitive information and online security. This study proposes a comprehensive approach that combines Chi-squared and XGBoost feature selection methods with logistic regression and Decision Tree classification models. By leveraging these techniques, it aims to achieve accurate and reliable identification of phishing websites while maintaining interpretability and generalizability. The findings underscore the importance of strategic feature selection and model building in enhancing the effectiveness of phishing detection systems. The results demonstrate promising outcomes, with both logistic regression and Decision Tree models exhibiting high accuracy in distinguishing between legitimate and phishing websites. Additionally, the key challenges such as feature scaling have been addressed to mitigate potential biases and data leakage. This research contributes to the advancement of cybersecurity measures, offering insights and methodologies to combat the persistent threat of phishing in the digital landscape. © 2024 IEEE.

关键词： Phishing

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Effect of Varied Datasets on Training of a Segmentation Model Used in Visual Navigation

Effect of Varied Datasets on Training of a Segmentation Mode...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Wada, Marin Adachi, Miho Miyamoto, Ryusuke Graduate School of Science and Technology Dept. of Computer Science Japan Meiji University School of Science and Technology Dept. of Computer Science Kawasaki Japan

ISBN: (纸本)9798350324457

A visual navigation method based on results of semantic segmentation showed interesting results in previous researches. The most significant problem of the method is that the moving performance is affected by the segmentation accuracy, which strongly depends on the training data even though SOTA methods are adopted. To create high-quality dataset for this application without huge human efforts, the authors tries to generate datasets for semantic segmentation from a 3D scanned data composed of colored point clouds. In this study, we investigate what kinds of variations are effective to construct a classifier: variation of augmentation considering shadows, shooting angles, and shooting locations. Experimental results using actual images for evaluation and generated dataset for training captured at the course of Tsukuba Challenge, which is the famous competition for autonomous moving robots in Japan, showed that adding shadows in training datasets improved the mIoU but random changes to the shooting angle and location did not always work well. By the result, it is shown that augmentation considering the characteristic of the target environment becomes important for practical use. © 2023 IEEE.

关键词： 3D scanned data dataset generation semantic segmentation visual navigation

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Simulator for Swarm Feature Selection Algorithms for Classification Problem 2

Simulator for Swarm Feature Selection Algorithms for Classif...

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2nd IEEE International Conference on Interdisciplinary Approaches in technology and Management for Social Innovation, IATMSI 2024

作者： Kulkarni, Mayuri Diwakar Shahade, Makarand Deore, Shailesh Khandekar, Nilesh Bhavsar, Ishwari Garud, Jayesh Ahirrao, Kunal SVKM's Institute of Technology Dept. of Computer Engineering Dhule India SSVPS's B.S. Deore CoE Dept. of Computer Engineering Dhule India

ISBN: (纸本)9798350360523

Swarm Intelligence (SI) algorithms address difficult optimization and decision-making issues using cues from collective behavior. These behaviors are inspired by social creatures like ants, bees, and birds. Using these creatures' behaviors algorithms have demonstrated amazing promise in several fields, such as distributed computing, robotics, and optimization. Assessing SI algorithms' performance under various conditions and scenarios is still a significant difficulty, though. We introduce a new Swarm Intelligence Algorithm Performance Analysis Simulator (SIAPAS) to tackle this issue. This proposed simulator introduces a novel approach to automating the process of selection for Swarm Intelligence (SI) algorithms on datasets. The system empowers users to upload datasets into a specialized simulator, which then orchestrates a comparative analysis using a suite of SI algorithms. The simulator evaluates and selects the most suitable algorithm based on the dataset's characteristics and problem domain. This provides the optimal selected features by the respective algorithms based on measured parameters. © 2024 IEEE.

关键词： Swarm intelligence

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