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共 25908 条记录，以下是221-230 订阅

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LiCT-Net: Lightweight Convolutional Transformer Network for Multiclass Breast Cancer Classification

LiCT-Net: Lightweight Convolutional Transformer Network for ...

引用

2024 IEEE Region 10 Conference, TENCON 2024

作者： Babita Nayak, Deepak Ranjan Malaviya National Institute of Technology Dept. of Computer Science and Engineering Jaipur India

ISBN: (纸本)9798350350821

Classification of multiclass breast cancer through histopathological images is indispensable and poses daunting challenges due to color inconsistencies, high appearance variations, and large inter-class similarities. Regardless of the success of traditional convolutional neural networks (CNNs) and vision transformers (ViTs), they often fail to capture intricate patterns and local contextual information, respectively, while demanding high computational resources and data requirements. To mitigate these issues, this paper proposes a lightweight convo-lutional transformer network named LiCT-Net for multiclass breast cancer classification. The LiCT-Net introduces a local-global spatially-aware transformer layer and integrates it with a pre-trained FastViT model to effectively capture global and local contextual features, thereby facilitating learning fine-grained and intricate lesion patterns from histopathological images. The LiCT-Net is validated on a benchmark dataset and the experimental results and comparative analysis demonstrate its effectiveness. In specific, it achieves a higher accuracy of 96.16 %, 95.62 %, 95.25 %, and 94.21 % on 40×, 100×, 200×, and 400× magniflcations respectively, © 2024 IEEE.

关键词： Convolution

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Hate Speech Detection in Roman Urdu using Machine Learning Techniques 5

Hate Speech Detection in Roman Urdu using Machine Learning T...

引用

5th International Conference on Advancements in Computational Sciences, ICACS 2024

作者： Nasir, Sarah Seerat, Ayesha Wasim, Muhammad University of Management and Technology Dept. of Computer Science Sialkot Campus Lahore Pakistan

ISBN: (纸本)9798350394764

In recent years, the spread of hate speech on social media has been a major source of discomfort. Hate speech may be extremely harmful, resulting in violence, discrimination, and even genocide. The problem is particularly acute in the case of the Roman Urdu language, where the use of hate speech is widespread. To address this issue, there's a growing demand for effective methods to identify hate speech on social media. While many researchers have focused on European languages, few have worked on South Asian languages, such as Roman Urdu, which are widely used in the subcontinent. In this study, we contribute by developing a methodology to detect hate speech in Roman Urdu at two levels. First, we classify the social media content into neutral and hostile categories. Secondly, we classify the hostile content as offensive and hate speech. We use a benchmark corpus (HS-RU-20) to evaluate the proposed methodology and the two-level classification. Furthermore, we analyze the word and character level features along with six supervised learning models (logistic regression, multinomial bias, KNN, random forest, SVM, and convolutional neural network (CNN)). The results show that logistic regression performed better in terms of accuracy, at 81% on neutral-hostile, and outperformed offensive-hate speech classification with an accuracy of 87%. © 2024 IEEE.

关键词： Logistic regression

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Harnessing Machine Learning for Accurate Corn Leaf Disease Classification 1

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

引用

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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Detecting Deceptive URLs: Exploring Logistic Regression and Decision Tree Models

Detecting Deceptive URLs: Exploring Logistic Regression and ...

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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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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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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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Hybrid Music Recommendation System combining Neo4j GraphDB based and ML-based Approach 4

Hybrid Music Recommendation System combining Neo4j GraphDB b...

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4th International Conference on Emerging Systems and Intelligent Computing, ESIC 2024

作者： Sathyajit, Prithviraj Thakur, Pallavi Sardar Patel Institute of Technology Dept. of Master of Computer Applications Mumbai India

ISBN: (纸本)9798350349856

Due to the significant expansion of the internet, particularly the World Wide Web, there is now an extensive collection of music accessible online. Recognizing the vastness of this musical landscape, it has become imperative to go beyond traditional search functionalities and introduce a service that not only facilitates user searches but also provides tailored music recommendations. This research paper presents an innovative approach to music recommendation by combining machine learning (ML) techniques with graph database technology. The proposed system utilizes ML to generate recommendations based on audio features extracted from a vast music dataset. The recommendation engine employs a clustering pipeline and cosine distance metrics to identify songs with similar characteristics. Additionally, the system leverages the capabilities of Neo4j, a graph database, to store and query relationships between songs deemed similar by their audio features. The integration of Neo4j enhances the recommendation process by allowing the system to consider not only audio feature similarities but also graph-based relationships. This hybrid approach aims to provide users with more diverse and contextually relevant music recommendations. The paper outlines the architecture of the system, including data preprocessing, ML model training, and the incorporation of Neo4j for storing and querying music relationships. Evaluation results demonstrate the effectiveness of the combined ML and graph-based recommendation system, offering a promising direction for advancing personalized music recommendation systems. This research contributes to the intersection of ML and graph databases in the field of music recommendation, offering a novel perspective on enhancing recommendation quality through diverse data representations. © 2024 IEEE.

关键词： Music

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

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

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

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

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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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