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Analysis of Prediction of Pneumonia from Chest X-ray images using CNN and Transfer Learning 5

Analysis of Prediction of Pneumonia from Chest X-ray images ...

引用

5th IEEE International Conference for Emerging Technology, INCET 2024

作者： Vinay, K. Shashanka Kodipalli, Ashwini Swetha, P. Kumaraswamy, S. Dept. of Computer Science and Engineering Global Academy of Technology Karnataka Bengaluru India Dept of Artificial Intelligence and Data Science Global Academy of Technology Karnataka Bengaluru India

ISBN: (纸本)9798350361155

A good design for image classification problems is the CNN architecture you presented, which consists of 5 convolution blocks followed by 4 fully connected layers. From the input X-ray images, the convolutional blocks extract pertinent features, and the fully connected layers assist in determining the final classification based on those learned features. You have integrated various approaches to improve the performance of your model. The inputs to each layer are normalized through batch normalization, which can speed up training and enhance generalization. By removing certain neurons at random during training, dynamic dropout helps avoid overfitting. L2 regularization weight decay and learning rate decay are two efficient strategies for preventing overfitting and enhancing the model's capacity to expand to new data. Popular optimization algorithm Adam optimizer effectively neural network training. For binary classification problems like the diagnosis of pneumonia, the loss function for binary Cross-Entropy is the best option. To determine your model's efficacy, you must validate it using benchmark datasets that are available to the general public. You can evaluate your model's effectiveness by comparing its performance to that of current methods by conducting experimental investigations on these datasets. Your model performs well as evidenced by accuracy scores of 90.93%, 89.17% for multi-class classification and binary classification. tasks. Automated methods, such as the one you suggested, might help medical practitioners recognize pneumonia and spot diseased spots in chest X-ray pictures. However, it's crucial to remember that automated systems shouldn't take the place of professional radiologists' and doctors' skills and judgment;rather, they should be used as supportive tools. Medical To ensure accurate diagnosis and suitable patient care, specialists should always review and interpret the system's data. It's also crucial to take into account potential drawback

关键词： Convolutional neural networks

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Intelligent Traffic Management System: An Advanced Solution 7

Intelligent Traffic Management System: An Advanced Solution

引用

7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Rushi, Tankala Zamaan, Shaik Mohammad Pravin, A. Sathyabama Institute of Science And Technology Dept. of Computer Science And Engineering Chennai India

ISBN: (纸本)9798350359299

The Intelligent Traffic Management System (ITMS) is a cutting-edge solution designed to address multiple aspects of traffic management and safety. This advanced system incorporates various technologies to tackle issues such ashelmet compliance, traffic signal violations, number plate identification, cell phone usage tracking, and proximity-based police station alerts. By leveraging the power of artificial intelligence and machine learning algorithms, the ITMS can accurately detect and enforce helmet compliance among motorcyclists, ultimately reducing the risk of head injuries in road accidents. Furthermore, the system utilizes advanced video analytics to detect violations of traffic signals, ensuring that motorists adhere to traffic rules and regulations. Additionally, the ITMS employs automatic number plate recognition (ANPR) technology to quickly and efficiently identify vehicles with illegal or expired license plates, enabling law enforcement agencies to take appropriate action. Moreover, the system includes a cell phone usage tracking feature which uses computer vision techniques to identify drivers who are using their phones while driving, contributing toa decrease in distracted driving incidents. Lastly, by introducing proximity-based police station alerts, the system offers a comprehensive approach to address challenges and improve efficiency, the ITMS enables prompt response from law enforcement agencies by automatically notifying the nearest police station in the event of a traffic violation or accident. In summary, the Intelligent Traffic Management System provides a comprehensive and holistic approach to management and safety through itsfeatures of helmet compliance, traffic signal violation detection, number plate identification, cell phone usage tracking, and proximity-based police station alerts. © 2024 IEEE.

关键词： Traffic signals

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Optical Mark Recognition with Object Detection and Clustering 6

Optical Mark Recognition with Object Detection and Clusterin...

引用

6th International Conference on Electrical Engineering and Information and Communication Technology, ICEEICT 2024

作者： Tabassum, Kaifa Rahman, Zhalok Shahjalal University of Science and Technology Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350385779

OMR, or optical mark recognition, is the automated analysis of human-marked documents. Presently, many competitive examinations rely on multiple-choice questions, and the answers to these questions are recorded in OMR sheets. In this study, we present an automated answer detection system from an OMR sheet image using computer vision techniques. Typically, evaluating an OMR sheet from an image involves scanning, extracting data, and interpreting the results through various image processing techniques. However, traditional approaches have limitations. They cannot detect empty responses or recognize multiple choices properly without a specified layout or total number of questions stated. We took a unique approach by employing an object detection model, YOLOv8n, to detect and classify OMR marks. To address overlapping bounding boxes, we implemented the DBSCAN clustering algorithm to effectively group the columns and determine question order. Our system is designed to work with multiple layouts, as it can detect columns through clustering. Furthermore, it can identify unanswered questions as well as multiple marked options. To train and evaluate our model, we created a custom dataset with five different OMR sheet layouts. We achieved a remarkable precision of 96.5%, a recall of 99.8%, and an mAP (mean average precision) of 97.3%. These results demonstrate the efficacy of our approach to OMR sheet analysis. © 2024 IEEE.

关键词： Object detection

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Comprehensive Women's Health Risk Assessment Platform 2

Comprehensive Women's Health Risk Assessment Platform

引用

2nd International Conference on Artificial Intelligence and Machine Learning Applications, AIMLA 2024

作者： Navya, S. Saha, Sharmistha Nagarajan, Deepa Sathyabama Institute of Science and Technology Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350349221

The goal of this research is to develop a machine learning-based method for predicting several diseases in women, with an emphasis on polycystic ovarian syndrome (PCOs), diabetes, heart disease, and breast cancer. Timely and accurate prediction of these disorders is essential for early intervention and prevention due to their impact on women's health and prevalence. Predictive models are trained using a wide variety of medical data, including genetic markers, lifestyle factors, demographic data, and clinical test findings. To find patterns and connections between these predictors and the incidence of diseases, machine learning methods are used, such as decision trees, random forests, and support vector machines. The models are able to forecast with accuracy the probability that a woman would develop one or more of these diseases by efficiently obtaining and evaluating data from these datasets. Metrics including accuracy, precision, recall, and F1-score are used to assess the models' performance;the outcomes demonstrate the models' promising levels of predictive power. The creation of a system like this can facilitate targeted screening and preventive measures, assist medical practitioners in identifying high-risk individuals, and eventually lead to better patient outcomes. © 2024 IEEE.

关键词： Diseases

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ML-JET: A Benchmark dataset for Classifying Heavy Ion Collisions

ML-JET: A Benchmark Dataset for Classifying Heavy Ion Collis...

引用

2025 IEEE/CVF Winter Conference on Applications of computer Vision Workshops, WACVW 2025

作者： Mehryar, Haydar Mao, Chengzhi Schwiebert, Loren Wayne State University Dept. of Computer Science United States Columbia University Dept. of Computer Science United States

ISBN: (纸本)9798331536626

Understanding relativistic heavy ion collisions is important for studying the evolution of the universe. Models of these collisions require hyperparameter tuning to accurately reproduce experimental results. Traditionally, Bayesian analysis, which is costly and non-scalable, has been used for this tuning. Deep learning is being explored as a potential alternative, although it is still in the early stages. In this work, we propose a novel benchmark dataset for anomaly detection in visual data generated from relativistic heavy ion collisions using the JETSCAPE framework. This dataset consists of 10.8 million jet event images, enabling the application of computer vision techniques to this domain. Our dataset converts complex physics simulations into event images, which are compatible with standard vision classifiers. Using the standard Convolutional Neural Networks (CNN), our initial results attain a 92% accuracy in energy loss module classification, while concurrently accelerating the tuning process by an order of magni-tude and saving millions of CPU/GPU hours over Bayesian analysis. This work demonstrates the potential of leveraging AI-based anomaly detection techniques to advance both physics and computer vision, offering a scalable solution for identifying anomalies in particle collision data. © 2025 IEEE.

关键词： Deep learning

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Augmenting Underwater Plastic Detection: A Study with YOLO-V8m on Enhanced datasets 3

Augmenting Underwater Plastic Detection: A Study with YOLO-V...

引用

3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Reddy, Veeramreddy Bharath Kumar Basha, Shaik Sameer Swarnalakshmi, J. Hindustan Institute of Technology and Science Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350375190

Ocean animals and plants are in danger because there is a lot of trash in the water. This old way of keeping an eye on trash made of plastic in the ocean is hard to do, takes a long time, and can't be used to check the whole ocean. In this plan, the new deep learning algorithms, called You Only Look Once version 8 (YOLOv8), are used to find and sort plastics in the ocean right away. There's a new way to handle this subject. Everyone knows YOLOv8 for how fast and well it can find things. It has been changed in this study to deal with the problems that only happen when taking pictures underwater, like changing light, cloudy water, and different kinds of plastic. A unique set of labeled underwater pictures are used to help it find different kinds of plastic very accurately and quickly. They are different sizes and shapes. Together, YOLOv8 and underwater robotic platforms make a useful and adaptable way to always keep an eye on the ocean. This collects useful information that can be used to protect the environment and lower pollution. Better tools for keeping an eye on the environment are made possible by the study. It also shows how cutting edge AI can be used to solve big environmental problems. © 2024 IEEE.

关键词： Object detection

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Real Time Monitoring and Configuration Interface for Smart Home Device Management Using Streamlit and Pygame

Real Time Monitoring and Configuration Interface for Smart H...

引用

2024 International Conference on Advances in data Engineering and Intelligent Computing Systems, ADICS 2024

作者： Mani, G. Suma Krishna, G. Venkata Praveena, M.D. Anto Asha, P. Selvan, Mercy Paul Sathyabama Institute Of Science And Technology Dept. Of Computer Science And Engineering Chennai India

ISBN: (纸本)9798350364828

One of the helpful tool for measuring and managing home power consumption is one of the clever and creative tool for tracking the usage of Home Power Management System. The Goal of this system is to lower the power bills, also promote energy efficiency, and provide the users active control over how much energy they use. Smart meters, an intuitive and user-friendly smartphone app, and real-time energy monitoring sensors are some of the system's essential parts and these are the key parts of the system. Installing smart meters makes it possible to accurately track the power usage and provide data to the system. Users can be able to see their energy usage instantly and make smart decisions when real-time energy monitoring equipment is included. Further, the system uses advanced smart technology, analytics and machine learning algorithms to provide clients customized suggestions on how to save energy consumption. Homes may save a lot of money on electricity bills, help the environment, and have more control over how much energy they use by using our Smart Home Power Management System. © 2024 IEEE.

关键词： Smart meters

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Beyond Copy Paste: Plagiarism Detection using Machine Learning 7

Beyond Copy Paste: Plagiarism Detection using Machine Learni...

引用

7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Vasuteja, Akula Reddy, Alavalapalli Vasanth Pravin, A. Sathyabama Institute of Science And Technology Dept. of Computer Science And Engineering Chennai India

ISBN: (纸本)9798350359299

Since plagiarism is a major problem in both academic and professional settings, it is crucial to create effective plagiarism detection methods. Accuracy evaluation for classical models such as SVM, Naive Bayes, and Random Forests usually involves cross-validation, where the dataset is split into training and testing folds, and the accuracy for each fold is then computed. These techniques lack the neural network-like loss functions, but they can still be used to analyze model performance by charting accuracy against the number of folds. But in CNNs and RNNs, training entails updating a loss function over epochs through iterative optimization;for classification problems, this is usually categorical cross-entropy. The values of the loss function are monitored during training, and the convergence of the model is shown by graphing them against the number of epochs. Accuracy metrics are calculated in parallel for the training and validation stages, and the model's classification accuracy throughout successive epochs is displayed in graphs. The plagiarism detector provides users with a complete report that highlights plagiarized portions together with their original sources which results in 97.99% of accuracy in order to aid educators, researchers, and other professionals in preventing plagiarism. © 2024 IEEE.

关键词： Support vector machines

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Real-Time Dashboarding using Big data Tools 7

Real-Time Dashboarding using Big Data Tools

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Sanjay, R. Pulakhandam, Dheeraj Nirmalrani, V. Sathyabama Institute of Science And Technology Dept. Computer Science And Engineering Chennai India

ISBN: (纸本)9798350359299

Real-time dashboarding software empowers organizations to harness the burgeoning IoT data stream, transforming raw sensor readings into actionable insights through a secure and scalable architecture. data from connected devices seamlessly flows via lightweight protocols like MQTT and is managed by a robust streaming platform like Kafka. Big data tools like Spark then extract meaning from this high-velocity data, preparing it for visualization on dynamic dashboards hosted by cloud platforms. These user-centric dashboards track key metrics, identify trends, and drive informed decision-making, but tackling challenges like scalability, security, and data quality is crucial to unlock the full potential of this transformative technology. © 2024 IEEE.

关键词： data visualization

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A Novel Method to Detect Oral Carcinoma Using Box Annotation Based on YOLO Model

A Novel Method to Detect Oral Carcinoma Using Box Annotation...

引用

2024 International Conference on Advances in Computing, Communication, Electrical, and Smart Systems, iCACCESS 2024

作者： Laila, Shahara Ema, Romana Rahman Galib, Syed Md. Jamil, Abu Rafe Md Uddin, A. F. M. Shahab Jashore University of Science and Technology Dept. of Computer Science and Engineering Jashore Bangladesh

ISBN: (纸本)9798350350289

Oral carcinoma affects millions of individuals globally and is a major public health problem due to its high fatality rate. Treatment effectiveness depends on early detection and improves survival rates. An effective method for automated oral cancer screening is deep learning. The paper's primary goal is to use deep learning techniques to advance the state of oral cancer diagnosis, specifically YOLOv5 and YOLOv7. We aimed to achieve accurate detection by leveraging bounding box annotations for both two distinct types of data: collected clinical data and benchmark data of oral carcinoma. The proposed methodology encompasses data collection, preprocessing, expert-driven bounding box annotation and the use of cutting-edge deep learning models for lesion identification, YOLOv5 and YOLOv7. Bounding box serving as expert annotations are meticulously added to pinpoint cancerous regions within the images. Subsequently, a comprehensive evaluation is conducted, measuring the performance of both models based on precision, recall rate (sensitivity) and mean average precision (mAP). Our findings reveal that YOLOv5 outperforms YOLOv7 in terms of oral cancer detection, demonstrating superior precision and recall rates. In the evaluation of this research, the YOLOv5 model demonstrated strong performance metrics on both clinical and lip and tongue cancer datasets. For the lip and tongue cancer dataset, the YOLOv5 model achieved a precision of 97.2%, a mAP (mean Average Precision) of 97.7% and a recall of 96.3%. © 2024 IEEE.

关键词： Clinical research

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