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Breast Cancer Tumor Detection Using Deep Learning Algorithms 4

Breast Cancer Tumor Detection Using Deep Learning Algorithms

引用

4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Sathishkumar, R. Janaki, R. Mahendra Engineering College Department of Computer Science and Engineering Namakkal India

ISBN: (纸本)9798350352931

Tumor classification is a crucial aspect of breast cancer diagnosis and treatment planning in medical imaging. Background: classification approaches often struggle with noise in samples, which reduces segmentation accuracy, affects feature selection, and hinders overall performance. Methods: a Graphical Neural Network (GNN)-based framework consisting of four key phases. First, images from the BreaKHis database are preprocessed using the Wiener filter to effectively reduce noise. Second, the watershed algorithm is applied for accurate image segmentation, enhancing the extraction of regions of interest. Third, Sequential Feature Selection (SFS) is utilized to identify optimal features without dimensionality loss. Finally, a GNN is employed for classification, leveraging its ability to model relationships between features and accurately classify tumors as benign or malignant. Achieving 98.26% accuracy, the proposed framework outperforms existing techniques, offering a robust and efficient tool for clinical breast cancer diagnostics. © 2024 IEEE.

关键词： Wiener filtering

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Cogniwealth: Revolutionizing Finance, Empowering Investors, and Shaping the Future of Wealth Management 5

Cogniwealth: Revolutionizing Finance, Empowering Investors, ...

引用

5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Ramyadevi, R. Sasidharan, G. Saveetha Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350383522

Predicting financial growth for future investment is the system that is used to take all kinds of finance data and store that data in the vector database and the Llama 2 model or the system is considered as a financial advisor and if we ask some investment question or any related question towards finance it will search on the data user provided before and the response will be formatted such a way that the response is coming from a human advisor because it uses Natural language processing(NLP) to understand the human nature and respond to there emotion and the system can be used by fund researchers and a laymen user who want to invest money in any finance related sector and this system can be feed with users wanted data and for front-end the react js is used and it is a frontend library and it is single page application and it is component based library and for backend Django is used and Llama 2 and pinecone db for storing vectors of the collected data. © 2024 IEEE.

关键词： Embeddings Finance Assistant Llama-2 Semantic Search

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Medicab emergency services

Medicab emergency services

引用

International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Sasikumar, S. Tejaswi, G. Sakthi Aishwarya, S. Subanu, K. Department of Computer Science and Engineering Saveetha Engineering College Chennai India

ISBN: (纸本)9781032669663

We created an Android app that allows users to access the medicab emergency services facility because there is a demise in India for a minute of the day. The concept suggests utilizing an app to let patients schedule transportation to hospitals. Patients can upload their current location and destination to the program or locate themselves. The patient can then evaluate the options and travel times of each transport across the area to choose the best one after the system displays nearby available ambulances. The application takes the ambulance driver to his destination, and he must confirm the reservation made. Administrators have access to all core data and are in charge of calling and querying operations. The app responds by pressing one button, at which point it notifies the nearby ambulance control center with the user’s information and location through GPRS. After collecting location coordinates from GPS devices, the software utilizes the Google Street View interface for application programming API to show ambulance details on the smart-phone’s Google Map client. © 2024, CRC Press/Balkema. All rights reserved.

关键词： Hospitals

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Evaluation of Federated Learning Based on Malware Detection

Evaluation of Federated Learning Based on Malware Detection

引用

2024 International Conference on Integration of Emerging Technologies for the Digital World, ICIETDW 2024

作者： Jeyanth, K. Harishkar, B. Ponmani, S. Anand, K. India Rajalakshmi Engineering College Computer Science Engineering Chennai India

ISBN: (数字)9798331541170

ISBN: (纸本)9798331541170

FL-LR, or Federated Learning Logistic Regression, enhances cybersecurity by using Federated Learning to detect malware in executable files. This approach allows decentralized devices to collaborate on training a Logistic Regression model without sharing raw data directly. Each device holds part of the malware dataset, ensuring data confidentiality. FL-LR's effectiveness is compared with Support Vector Machines and Random Forests using metrics like accuracy, precision, memory usage, F1-score, and AUROC. Results show FL-LR performs well while preserving data privacy. The study also examines FL-LR's scalability and convergence in federated settings, demonstrating its suitability for large-scale malware detection. Overall, FL-LR with Logistic Regression shows promise for enhancing cybersecurity through Federated Learning while protecting sensitive data. © 2024 IEEE.

关键词： Federated learning

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A Novel Approach for Classification of Crops and Weeds using Deep Learning

A Novel Approach for Classification of Crops and Weeds using...

引用

2024 IEEE International Conference on Electronic Systems and Intelligent Computing, ICESIC 2024

作者： Ram Prakash, L. Pravinesh, H. Ariyamala, V. Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331522988

Weed detection is very important in modern agriculture, and timely and fast identification of weeds helps to reduce crop losses that have a positive effect on the yield. The classical methods of weed management involve manual weeding or broadcast spraying with herbicide, and both have laborious, expensive, and dismaying effects on the environment. The demand has never been higher for automatic weed detection systems in agriculture. Abstract: This project aims to build an automated weed detection system with YOLOv8, one of the most state-of-the-art object detection algorithms that excels in real-time accuracy. The system utilizes high-resolution drone and camera images of fields to pinpoint the specific plants growing in a crop, enabling more selective herbicide application and minimizing the need for manual labor from cropping operators. The design of YOLOv8 ensures high-speed and high-precision performance, making it ideal for use as a real-time detection system in the field. We train our model on a home-grown dataset of approximately 8k crop and weed images (gm), incorporating additional data augmentation for enhanced robustness. We will assess performance using evaluation metrics like precision, recall, F1-score, and mean average precision (mAP). © 2024 IEEE.

关键词： Deep learning

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Comparative Analysis of Potato Leaf Disease Classification Using CNN and ResNet50 7

Comparative Analysis of Potato Leaf Disease Classification U...

引用

7th International Conference on Data science and Its Applications, ICoDSA 2024

作者： Bharti, Riya Srivastava, Vivek Bajpai, Abhishek Sahu, Shalinee Rajkiya Engineering College Department of Computer Science Engineering Kannauj India

ISBN: (纸本)9798350365351

Plant diseases are a significant obstacle to crop quality and quantity, potentially impacting the food supply. Early identification of plant diseases through precise or nearly accurate detection methods can enhance food production quality and minimize economic losses. In recent years, deep learning models have brought tremendous change and improvement in recognizing the accurate results of image classification and object detection systems. In this project, three consecutive stages are employed to identify the type of disease. These stages include preprocessing, feature extraction, and classification using a Convolutional Neural Network (CNN) and ResNet50. These diseases predominantly impact green and living plants. This paper compares two convolutional neural network (CNN) architectures, a custom-designed CNN with six layers and ResNet50 for classifying plant leaf diseases. The dataset consists of 1,706 images, having six classes for training and 567 for validation. Both two architectures were trained and evaluated on the same dataset. Performance metrics such as training and validation accuracy were used to compare the effectiveness of the models in classifying plant leaf diseases. The results indicate that ResNet50 achieved an accuracy of 98.36%, whereas the custom CNN attained an accuracy of 94.29%. This summarises that ResNet50 performs better than the custom CNN, highlighting the strengths and weaknesses of each architecture in this specific application. © 2024 IEEE.

关键词： Convolutional neural networks

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E-voting system with secure blockchain alert on data tampering

E-voting system with secure blockchain alert on data tamperi...

引用

International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Julian, Anitha Priya, M.S. Lekshmi Chamundeeswari, V. Vijaya Department of Computer Science and Engineering Saveetha Engineering College Chennai India

ISBN: (纸本)9781032669663

With the growth of Blockchain technology in the second decade of the new millennium, numerous applications throughout the globe have been interested in intriguing new uses. The subject of electronic voting is one of the difficult uses of blockchain technology. Since the election is being conducted offline, security is of the utmost importance. An organization still has full access to the current electoral system’s centralized infrastructure. The entity that controls the database and has complete control over the system is mostly to blame for the issues because it is using antiquated election systems. A significant factor that seriously affects the system is database manipulation. The use of blockchain technology is one of the efficient solutions to this issue. Block chain technology is the best option because it has a decentralized structure and a shared database. This paper explores the potential for using Blockchain technology in e-voting systems to enhance the voting process by addressing issues of trustlessness, privacy, and security with effective face authentication techniques using Haar cascade and Support Vector Machine Algorithms. © 2024, CRC Press/Balkema. All rights reserved.

关键词： Blockchain

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Deep Learning - Driven RPA Bot for Automated Evaluation of Handwritten Examination Papers Using Gen AI

Deep Learning - Driven RPA Bot for Automated Evaluation of H...

引用

2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Duraimurugan, N. Vaishnavi Sri, S.M. Subhikshaa, S. Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331543617

The incorporation of Robotic Process Automation (RPA) and deep learning in the educational evaluation system brings a strong, automated solution for assessing handwritten exam assessments. Educational institutions have exponentially started using RPA systems for monotonous activities like data storage and management, but the capacity extends far beyond these activities. This paper gives an automated solution for evaluating handwritten examination papers in educational institutions by combining advanced deep learning models and RPA (Robotic Process Automation). Using CNNs (Convolutional Neural Networks) and BiLSTM (Bidirectional Long Short-Term Memory) networks, the system converts handwritten responses into machine-readable text, trained to various handwriting styles. For grading, a Gen-AI model is used to assess the answers recognized by CNN and BiLSTM. This provides precise grading that aligns closely with human correction. RPA does end-to-end automation, from scanning and processing papers through deep learning models for text recognition to grading and data management thus reducing human effort, enhancing consistency, saving time and improving efficiency in the evaluation process. This technique streamlines examination, allowing teachers and professors to focus more on personalized instruction and academic development. © 2024 IEEE.

关键词： Convolutional neural networks

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ECG - Driven Heart Disorder Profiler using Machine Learning Techniques 1

ECG - Driven Heart Disorder Profiler using Machine Learning ...

引用

1st IEEE International Conference on Electronics, Computing, Communication and Control Technology, ICECCC 2024

作者： Manimaran, V. Shanthi, N. Aravindhraj, N. Aatarsh, K.M. Adharshini, G. Gokul, P. Kongu Engineering College Department of Computer Science and Engineering Erode India

ISBN: (纸本)9798350371802

Right now, heart illness is the driving cause of passing around the world and its frequency is on the rise. To precisely analyze heart illness in its early stages some time recently a heart event occurs, it is troublesome to do so. There is a large amount of data on heart disease available in the healthcare sector such as clinics, hospitals, etc., but it is not easy to process this data to identify hidden patterns. Machine learning techniques can help to transform this medical data into valuable information. Machine learning can be used to design support system that can learn from their experiences. The aim of the research is to furnish the heart illness diagnosis in the most precise way. The proposed work uses machine learning and feature selection algorithms to compute results using the hybrid model. Methods like the Random Forest, Boruta, decision tree, logistic regression (LR), support vector machine (SVM), logistic regression with SVM, and hybrid models that combine logistic regression with SVM and Random Forest were used in this process. The assessment includes both individual and ensemble models and covers all heart disease datasets. Hybrid models are evaluated across multiple datasets using key metrics such as true positive rate, specificity, accuracy. The proposed framework's results are analyzed with following extensive experimentation. The study found that the proposed hybrid machine learning models outperform individual models and approaches in terms of accuracy, sensitivity, and specificity across all heart disease datasets. © 2024 IEEE.

关键词： Neural networks

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Improved Learning Based on GenAI 2

Improved Learning Based on GenAI

引用

2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Borah, Asha Rani Nischith, T.N. Gupta, Saksham New Horizon College of Engineering Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350327533

This research introduces a novel strategy to improve learning outcomes by incorporating generative artificial intelligence (AI) into educational frameworks. Using sophisticated machine learning algorithms, GenAI analyzes each learner's profile and modifies educational tactics and content in real time. This flexible and dynamic learning environment offers a customized experience that is unmatched by conventional educational models, accommodating a wide range of learning styles, interests, and aptitudes. With the growing importance of individualized learning, GenAI presents itself as a transformative invention with the potential to completely change the face of education. This article highlights how GenAI has the ability to completely change the way we approach and provide education by outlining its architecture, the machine learning algorithms that underpin it, and how it has transformed educational practices. © 2024 IEEE.

关键词： Advanced learning Generative AI (GenAI) Stable diffusion

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