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An Automated Glaucoma Detection using DenseNet for ANSAN 15

An Automated Glaucoma Detection using DenseNet for ANSAN

引用

15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Harshni Sri, K.S. Sri Kailaash Kumar, S. Bindu, Avadhani Thangavel, Senthil Kumar Somasundaram, K. Parthasaradhi, Sathyan Shanmugam, Selvanayaki Kolandapalayam Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Coimbatore India Amrita Vishwa Vidyapeetham Amrita School of Physical Sciences Department of Mathematics Coimbatore India Sathyan Eye Hospital Coimbatore Glaucoma Foundation Coimbatore Ramanathapuram India Ashland University Department of Mathematics and Computer Science OH United States

ISBN: (纸本)9798350370249

Glaucoma, a sight-threatening condition, results from heightened pressure on the optic nerves due to fluid accumulation in the eyes. This pressure damages optic nerves crucial for vision, emphasizing the urgency of managing fluid build-up to prevent irreversible vision loss. To improve detection accuracy and reduce human error, we propose an automated glaucoma detection method using deep learning techniques. By employing a pre-trained DenseNet-121 model and data augmentation techniques to achieve a remarkable accuracy of 93% in effectively discerning between healthy and glaucomatous retinas. This demonstrates the potential of deep learning models such as DenseNet in revolutionizing glaucoma diagnosis by providing reliable and timely detection by paving a way for early intervention strategies that can significantly enhance patient outcomes © 2024 IEEE.

关键词： Automated Glaucoma detection classification CNN deep-learning DenseNet Retinal image classification

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Predicting Knee Osteoarthritis Progression from Radiographic images: A Deep learning Approach for Early Detection and Intervention 15

Predicting Knee Osteoarthritis Progression from Radiographic...

引用

15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Nanwani, Jaya G. Bondre, Shweta V. Bondre, Vipin Bali, Chetna Shri Ramdeobaba College of Engineering and Management Nagpur India Yeshwantrao Chavan College of Engineering Nagpur India Datta Meghe Institute of Higher Education and Research Sharad Pawar Dental College and Hospital Wardha India

ISBN: (纸本)9798350370249

Knee osteoarthritis (KOA) is a frequent and difficult medical illness to manage in the elderly. Conventional KOA diagnosis is based on manual X-ray analysis classified using the Kellgren-Lawrence (KL) grading system. However, this subjective method involves extensive physician knowledge, time investment, and is naturally prone to interobserver variability. Leveraging advances in machine learning, researchers are investigating deep neural network (DNN) models for automatic and accurate KOA diagnosis from imaging data. This work recommends using a pre-trained EfficientNetB5 architecture for KOA classification on the Osteoarthritis Initiative (OAI) dataset. Binary and three-class classification tasks were utilized to determine the presence/absence and severity of KOA, respectively. The EfficientNetB5 model obtained a maximum classification accuracy of 78.18%, highlighting DNNs' promise for improved KOA diagnosis. © 2024 IEEE.

关键词： Arthroplasty

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SkyStrokes: Bridging Artistic Expression and Communication Barriers Through Aerial Digital Canvas 6

SkyStrokes: Bridging Artistic Expression and Communication B...

引用

6th IEEE international conference on Emerging Smart computing and Informatics, ESCI 2024

作者： Bommala, Harikrishna Suvekha, Raman Reddy, Orra Venkata Srikanth Kumar, Neerati Shiva Rokati, Sai Kiran KG Reddy College of Engineering and Technology Department of Computer Science and Engineering Telanagana Moinabad India KG Reddy College of Engineering and Technology 4th Year Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350306613

This endeavors to address pressing societal concerns by introducing innovative solutions. It confronts the challenges of communication barriers encountered by the hearing-impaired, the excessive reliance on smartphones, and the environmental impact of paper waste. Through the implementation of 'on-Air writing,' an application is proposed that tracks object movements, enabling users to draw on a computer screen by simply waving their hands. By merging technology and social responsibility, this initiative strives to empower the deaf and mute community while advocating for eco-conscious practices. In doing so, it not only revolutionizes communication but also encourages a more inclusive, tech-savvy, and environmentally aware world. © 2024 IEEE.

关键词： Computer Vision Gesture recognition Hand tracking Human-computer Interaction Machine learning Social Issues

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A Systematic Review of Data Augmentation Techniques in Natural Language Processing for Sentiment Analysis using Machine learning Techniques 4

A Systematic Review of Data Augmentation Techniques in Natur...

引用

4th international conference on Soft computing for Security Applications, ICSCSA 2024

作者： Patil, Uday Dhanaji Sudhakaran, Pradeep SRM Institute of Science and Technology Department of Computer Science & Engineering Tamil Nadu Kattankulathur603203 India Kolhapur India SRM Institute of Science and Technology Department of Computing Technologies Tamil Nadu Kattankulathur603203 India

ISBN: (纸本)9798331515720

Due to the enormous growth of mobile technology, Social media has created a crucial platform to express the feelings and opinions of people. Based on these opinion considerations, it is very beneficial for making strategic decisions in organizations, businesses, trading, political entities, and so on. To achieve effective improvement, sentiment analysis is taken into account, and the public opinion polarity for further reference. In this perspective, the main contribution of the study is to explore the benefits and limitations of various learning techniques under the evaluation of sentiment analysis. Generally, Sentiment analysis is evaluated by Natural language processing tasks with various learning techniques, to achieve effective generalization ability and efficiency, data augmentation techniques are integrated, which reduces the overfitting problem effectively. To explore the limitations of the traditional methods, 25 literary papers were taken into account to analyze various approaches, and methods under the utilization of distinct datasets. Together with this, the achieved evaluation metrics along with the achievements are also discussed briefly in the study. Additionally, the study provides the shortcomings and future scope of the conventional methods with a detailed description. The main objective of the survey includes knowledgeable categorization, which aims to explore a new model with the illustration of recent trends and achieve effective data augmentation in future analysis. © 2024 IEEE.

关键词： Adversarial machine learning

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Deep learning Solution For Crop Health Monitoring using CNN in Smart Agriculture

Deep Learning Solution For Crop Health Monitoring using CNN ...

引用

2024 international conference on Electrical, Electronics and computing Technologies, ICEECT 2024

作者： Dhyani, Ayush Singh, Harjot Rawat, Purvi Aeri, Manisha Chauhan, Avnendra Singh Ashish Dhondiyal, Shiv Graphic Era Hill University Department of Computer Science and Engineering Dehradun India Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350378092

The agricultural Sector has witnessed a transformative shift towards smart agriculture, with a significant emphasis on leveraging advanced technologies for efficient crop disease detection with context to this the paper explores the potential of modern technologies, particularly computer vision and machine learning, for the early detection and management of crop *** report explores the integration of Convolutional Neural Networks (CNNs) in smart agriculture to address the escalating challenges of crop protection. Focusing on the significant threat of crop disease faced during cultivation of different crops, the study emphasizes early detection using advanced technologies. A literature survey showcases successful disease identification models, and the proposed methodology outlines data collection, preprocessing, and CNN implementation. Results demonstrate CNN's effectiveness in Greencare with an accuracy of up to 95.71%, offering a robust framework for further advancements in agricultural image analysis. This research contributes to sustainable practices, promoting global food security through innovative crop disease detection systems. © 2024 IEEE.

关键词： Smart agriculture

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Elevating Disease Prediction: A Stacking Ensemble learning Approach 3

Elevating Disease Prediction: A Stacking Ensemble Learning A...

引用

3rd IEEE international conference on Distributed computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Kavitha, T. Trivikram, Kammara Manobhi Ram Reddy, B. Shashank, S. Sushmitha, H. Rahul, B. New Horizon College Of Engineering Dept. Of Computer Engineering Bangalore India

ISBN: (纸本)9798350318609

Timely and accurate disease diagnosis is critical for effective treatment and cost reduction. This project develops a web app utilizing machine learning to predict diseases from user-entered symptoms and recommend nearby specialists. An ensemble model stacking Logistic Regression, Random Forest, KNN, MLP, and SVM classifiers with XGBoost as the meta-learner was created using a diverse dataset of symptoms, diseases, and medical specialists. The Flask-based app features user authentication, symptom input, and Google Maps API integration for doctor recommendations. The ensemble model accurately predicts potential diseases and relevant specialists nearby, providing an interactive platform for instant diagnosis and treatment access to reduce complications and costs. Rigorous testing ensured seamless deployment, empowering early disease identification and access to appropriate healthcare. The ensemble achieved 97% accuracy, outperforming individual base learners, while KNN emerged as the top individual model with 92.3% accuracy. © 2024 IEEE.

关键词： Forecasting

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Text Semantic Matching Model Based on Adversarial Training and Multi-angle Features 3

Text Semantic Matching Model Based on Adversarial Training a...

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3rd international conference on Cloud computing, Big Data Application and Software engineering, CBASE 2024

作者： Huang, Xiaofan Beihang University Beijing China

ISBN: (纸本)9798331506582

Text semantic matching is a fundamental task in natural language processing, widely applied in areas such as information retrieval, question answering systems, and recommendation systems. Traditional matching methods primarily focus on shallow features, but recent advances in deep learning have significantly propelled the development of text matching techniques. In light of the challenges faced by existing models, this paper proposes a text semantic matching model based on adversarial training and multi-angle features (MATM). This model adopts a siamese network framework, integrating various neural network structures. It leverages the WoBERT pre-trained model to extract deep contextual semantic information and combines the strengths of BiLSTM and CNN in feature extraction. Additionally, the model introduces an attention mechanism to merge multi-angle semantic features and employs adversarial training to generate adversarial samples, enhancing the model's generalization ability. Experiments on two Chinese text semantic matching datasets, BQ and BDZD, demonstrate that the model achieves accuracy and F1-Score of 85.93% and 85.77% on BQ, 85.38% and 86.72% on BDZD, respectively, showcasing substantial competitiveness. © 2024 IEEE.

关键词： Adversarial machine learning

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Enabling Autonomous Digital Marketing: A Machine learning Approach for Consumer Demand Forecasting 5

Enabling Autonomous Digital Marketing: A Machine Learning Ap...

引用

5th IEEE international conference on computing, Power, and Communication Technologies, IC2PCT 2024

作者： Singhal, Kanika Singh, J.N. Sharma, Vishnu Galgotias University School of Computing Science & Engineering Uttar Pradesh India

ISBN: (纸本)9798350383522

This research surveys the integration of Artificial Intelligence (AI) and Machine learning (ML) to establish an autonomous framework for Digital Marketing, with a primary focus on revolutionizing traditional marketing strategies by forecasting and addressing consumer demands. The proposed approach aims to enhance decision-making processes through the application of advanced algorithms for precise demand predictions. Additionally, the study explores the efficacy of AI through ensemble machine learning, employing decision tree algorithms to optimize digital marketing indicators. The examination emphasizes the utilization of AI-powered ensemble ML to refine cost-effective strategies and maximize profits, using a dataset comprising 6561 possible tuples. Three collaborative ML approaches are working as algorithms to distinguish designs and relationships within the price data, contributing to strategic decision-making. This project uniquely illustrates the possible of replicated data to elevate cost-saving strategies in business contexts. The findings not only contribute to existing works on AI and ML requests in commercial but also highlight the transformative impact ML can have on commercial proprietors, production, and marketing personnel, extending implications to various businesses, counting transport, logistics, and trade, with significant prospects for improving overall performance of 91.75% in operational efficiency. © 2024 IEEE.

关键词： Artificial Intelligence Autonomous Framework Consumer Demand Forecasting Digital Marketing Machine learning

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Visual SLAM System Integrating Feature Extraction Based on Vision Transformer and Cloud-Edge Technology

Visual SLAM System Integrating Feature Extraction Based on V...

引用

2024 international conference on Intelligent computing and Robotics, ICICR 2024

作者： Fu, Bo Yao, Xulin School of Computer Science and Engineering Xi'An University of Technology Xi'an China

ISBN: (纸本)9798350372274

Deep learning-based visual SLAM (Simultaneous Localization and Mapping) has become one of the highly researched areas in recent years. Deep learning can be integrated with various modules of visual SLAM systems, including VO (Visual Odometry), mapping, feature extraction, localization, BA (Bundle Adjustment) optimization, loop closure, dataset handling, etc. In visual SLAM, traditional feature point extraction using deep learning networks is based on Convolutional Neural Network (CNN) architectures. This paper proposes a method that combines feature extraction based on the vision Transformer network with edge-cloud technology to improve the performance of visual SLAM systems. This method addresses scenarios where neural network-based SLAM systems encounter limited computing resources, frequent environmental changes, or require realtime response. Experimental results demonstrate that the fusion of feature extraction based on vision Transformer and edge-cloud technology significantly improves the localization, mapping, and real-time performance of visual SLAM systems. It exhibits excellent robustness and accuracy in various indoor and outdoor environments, indicating promising application prospects and dissemination value. ©2024 IEEE.

关键词： Convolutional neural networks

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Rainfall Prediction Enhancement Using SMOTE and Machine learning Algorithms 15

Rainfall Prediction Enhancement Using SMOTE and Machine Lear...

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15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Hemanth Kumar, R. Vishnu Prasad, B. Yadav, Himanshu Venugopalan, Manju Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

Rainfall prediction is not an easy task when utilizing conventional approaches. For many stakeholders, including people planning their daily lives, farmers caring their crop, and fishermen who depend on the weather for their livelihood, accurate rainfall forecast is essential. Predicting rainfall more accurately has been a promising use of machine learning (ML) models in recent years. Our proposed framework suggests using classification techniques like Random Forest, K-Nearest Neighbors (KNN) classifier, and Decision Tree to construct and implement an ML model for rainfall prediction. SMOTE, an efficient data-balancing technique is used for balancing the minority and majority classes of the data, as rainfall is not a uniformly distributed event, leading to an imbalanced dataset. With an astounding F1-score of around 0.91, a precision of about 0.89, a recall of about 0.94, and an accuracy of roughly 0.91, the Random Forest classifier outperformed the others. These results demonstrate how machine learning (ML) models could possibly be used in producing accurate and depewhich are effective forndable rainfall forecasts, showing significant improvements over conventional prediction techniques. © 2024 IEEE.

关键词： Prediction models

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