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Comparative Evaluation of Machine Learning Techniques for Breast Cancer Detection

Comparative Evaluation of Machine Learning Techniques for Br...

引用

2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Sharma, Atul Pati, Natasha Gourisaria, Mahendra Kumar Panda, Amiya Ranjan Patra, Sudhansu Shekhar Pattnayak, Parthasarathi KIIT Deemed to be University School of Computer Engineering Odisha Bhubaneswar India KIIT Deemed to be University School of Computer Applications Odisha Bhubaneswar India

ISBN: (纸本)9798350378092

Breast cancer is a primary cause of death for women. Millions of women worldwide were suspected with breast cancer diagnosis in 2022. But early-stage breast cancer is still treatable. Patients' recoveries become better when breast cancer is discovered early and treated quickly and efficiently. Machine learning algorithms have become an effective tool for precisely and successfully diagnosing breast cancer in recent years. This research focuses on the utilization of machine learning (ML) techniques. To achieve this, we looked into a variety of machine learning methods including XGBoost, Random Forest, K Nearest Neighbours, Naïve Bayes, Decision Trees, Logistic Regression, and Neural Networks. Metrics including accuracy, precision, F1score, recall, and balanced accuracy were used as indicators of the models. The result shows that K Nearest Neighbors and Naïve Bayes has more accuracy and can be effective in early detection of cancer. © 2024 IEEE.

关键词： Lung cancer

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Exploring Generative Adversarial Network for Image Synthesis: A Comprehensive Approach

Exploring Generative Adversarial Network for Image Synthesis...

引用

2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Pati, Natasha Sharma, Atul Gourisaria, Mahendra Kumar Bilgaiyan, Saurabh Patra, Sudhansu Shekhar Pattnayak, Parthasarathi KIIT Deemed to be University School of Computer Engineering Odisha Bhubaneswar India KIIT Deemed to be University School of Computer Applications Odisha Bhubaneswar India

ISBN: (纸本)9798350378092

In recent years, computer vision and machine learning have turned their attention to the research of using deep neural networks to produce realistic images. By using a sizable image collection to train the model, images can be produced at the pixel level. Creating anime images from a basic drawing presents an interesting research challenge. The domain of anime picture generation is studied in this article using the Generative Adversarial Networks (GAN) model. Unsupervised machine learning algorithms use a subset of artificial intelligence called GAN. Using a discriminator and generator, the model can create realistic anime faces. While the Generator is a network that produces images, the Discriminator determines if the image is produced or real. To produce anime faces of the finest quality, it places the highest priority on the thorough selection of superior datasets. Strict training is carried out using sophisticated methods and optimized hyper-parameters. Additionally, a comparative analysis with other generative picture creation methods is provided by the research. © 2024 IEEE.

关键词： Generative adversarial networks

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Placement Prediction Using the Artificial Neural Network (ANN) 3

Placement Prediction Using the Artificial Neural Network (AN...

引用

3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Priyadarsini, Jukta Badiger, Veena S. Sheetal Jacintha, Anitha DSouza Presidency College Department of Computer Applications Bengaluru India Presidency University Presidency College School of Engineering Bengaluru India

ISBN: (纸本)9798331515911

Placements are of utmost significance to academic organizations and college students. A strong foundation for the professional field is built up for the student beforehand, and a positive placement report gives a school or college a competitive edge in the educational marketplace. An automated method of statistical evaluation that streamlines the creation of analytical versions is machine learning. In this study, a computer that predicts whether a student might be located or not is the focus, based solely on the student's credentials, historical data, and experience. The performance is evaluated using students' datasets from presidency college and accuracy achieved for our model is 96%. © 2025 IEEE.

关键词： Adversarial machine learning

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House Price Prediction Using ML

House Price Prediction Using ML

引用

2024 International Conference on Sustainable Power and Energy, ICSPE 2024

作者： Kumar, Anurag Singh, Kalyani Raj, Aman Shakya, Aryan School of Computer Applications and Technology Galgotias University Greater Noida India School of Computer Science and Engineering Galgotias University Greater Noida India

ISBN: (纸本)9798350366051

With the use of several Python modules, this project gives us a general idea of how to forecast home prices using several machine learning models. This suggested model offers the most accurate prediction and is thought to be the most accurate model for determining home prices. This gives a quick overview that is necessary to forecast the cost of a home. Using the datasets we will utilize in our proposed model, this study aims to explain what and how the house pricing model functions with the aid of machine learning techniques. Finding the right moment to purchase a property and figuring out The purchase price of a house in a certain area is helped by a price *** objective of this machine learning task on house price prediction is to develop a machine learning model based on data in order to forecast house. © 2024 IEEE.

关键词： Prediction models

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Digital Watermark Techniques and Its Embedded and Extraction Process 3rd

Digital Watermark Techniques and Its Embedded and Extraction...

引用

3rd International Conference on Machine Learning, Internet of Things and Big Data, ICMIB 2023

作者： Das, Satya Narayan Panda, Mrutyunjaya Department of Computer Science and Engineering GIET University Odisha Gunupur India Department of Computer Science and Applications Utkal University Odisha Bhubaneswar India

ISBN: (纸本)9789819939312

The development of the digital communication system is increasing rapidly and all the manual data management process is massively converted to the digital processing system to make faster processing and reduce the valuable time of human users and increase productivity. Now the input and output system of the device is converted to a higher-level interface medium so that the device can communicate with the user and environment through audiovisual components and communicate like a human. Also in the present scenario devices are implemented by human society to deal with very secure and sensitive information like banking, the military, the research environment, the medical sector, etc. Hence there is a chance of misutilization of information through the device. Many Security using mathematical techniques like cryptography, digital signature, etc. are used to make secure information from the unauthentic person, but to protect the copywriting of the digital information watermark technology is used. Through the watermark, we can add the identification of the owner hence the other person can not claim or tamper with the information for its misutilization. In this proposed work we reviewed different techniques to create a watermark with the algorithm associated with it which helps the researcher extend the watermark technique in a better way. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Digital communication systems

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Predictive Modeling for Asthma Disease Detection: A Comparative Study of Machine Learning Algorithms 4

Predictive Modeling for Asthma Disease Detection: A Comparat...

引用

4th IEEE International Conference on Applied Electromagnetics, Signal Processing, and Communication, AESPC 2024

作者： Mukherjee, Arka Jena, Junali Jasmine Gourisaria, Mahendra Kumar Parui, Sricheta Patra, Sudhansu Shekhar Pattnayak, Parthasarathi School of Computer Engineering KIIT Deemed to Be University Odisha Bhubaneswar India School of Computer Applications KIIT Deemed to Be University Odisha Bhubaneswar India

ISBN: (纸本)9798331528171

Artificial Intelligence (AI) and Machine learning (ML) models have proven to be scalable approaches for handling several biomedical problems. Recent availability of high-quality datasets which captures various factors contributing to the respiratory disease, have enabled the development of robust models that deliver high accuracy and precision scores in early detection of respiratory diseases. Asthma is a common respiratory illness which causes shortness of breath, coughing, wheezing etc. This paper focuses on asthma disease detection. It makes two primary contributions: (1) an empirical evaluation of their performance on an asthma disease detection dataset with data mining and pre-processing techniques, and (2) the identification of the most effective approach for asthma disease detection based on rigorous evaluation using metrics such as precision, accuracy, recall, F-1, and F-beta scores. Cat Boost Classifier was found to be the best model which predicted asthma disease with a 96.04% accuracy. © 2024 IEEE.

关键词： Contrastive Learning

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Machine Learning Enhancements for Car Insurance Claim Prediction 15

Machine Learning Enhancements for Car Insurance Claim Predic...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Saikia, Deepratim Barua, Rion Gourisaria, Mahendra Kumar Bandyopadhyay, Anjan Mishra, Soumya Ranjan Bilgaiyan, Saurabh School of Computer Engineering KIIT Deemed to be University Odisha Bhubaneswar India School of Computer Applications KIIT Deemed to be University Odisha Bhubaneswar India

ISBN: (纸本)9798350370249

One of the biggest concerns of car insurance industry is efficiently managing car insurance claims while minimizing fraudulent submissions. This study explores the use of machine learning models in the prediction of auto insurance claims. Unlike traditional rule-based systems, which often struggle with intricate patterns and dynamic data, machine learning offers powerful, efficient, and often more accurate methods for predicting car insurance claims. By analyzing historical data on car insurance claims, various machine learning algorithms can be employed to develop a robust predictive model. This research highlights the pivotal role of machine learning in revolutionizing the insurance landscape. Accurate predictions empower insurers to allocate resources efficiently, streamline processes, and ultimately enhance the experience for policyholders. This study focuses primarily on enhancing claim processing efficiency. In terms of accuracy, we found the XG Boost performs the best classifier with an accuracy of 0.84. ©2024 IEEE.

关键词： Prediction models

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Signature-Based Batch Auditing Verification in Cloud Resource Pool 1

引用

1st International Conference on Advanced Computing, Machine Learning, Robotics and Internet Technologies, AMRIT 2023

作者： Goswami, Paromita Gogoi, Munmi Debnath, Somen Khan, Ajoy Kumar Department of Computer Engineering and Applications GLA University Mathura India Department of Information Technology Mizoram University Aizawl India Department of Computer Engineering Mizoram University Aizawl India

ISBN: (数字)9783031472244

ISBN: (纸本)9783031472237

Online cloud resource pool provides cloud users with a multitude of appealing developments in highly sought-after online scalable storage services for them to eager new creative and investment business benefits. Today, the majority of cloud data security research focuses on ways to increase the accuracy of outsourced data audits rather than internal and foreign foes who could hack a cloud server. Even though the data owner enviously envies the data auditing work of stored data to a trustworthy Third Party Auditor(TPA) to avoid communication as well as computational overhead of data when outsourcing to a reputable Cloud Service Provider, TPA is unreliable in a realistic setting. In this research, we offer a model for an effective data integrity system based on the ZSS signature, which secures data privacy in cloud storage. Finally, the actual results of the performance of the proposed prototype of the system model were tested and indicated greater efficiency of provided model in comparison to BLS signature. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Lakes

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A Network Optimized 3D Intercom Approach For UAV Communication Using 6G Network 1

A Network Optimized 3D Intercom Approach For UAV Communicati...

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1st International Conference on Intelligent Technologies for Sustainable Electric and Communications Systems, iTech SECOM 2023

作者： Premalatha, N. Gayathri, P. Sangavi, N. Ramesh, K. Nandhakumar, R. Samraj, Andrews Pg and Research Department of Computer Science and Applications Namakkal India Mahendra College of Engineering Department of Computer Science and Engineering Namakkal India

ISBN: (纸本)9798350371185

With such motivation, the routing optimization in 3D intercom facility is required when an UAV communication is focused. The ground stations control the autonomous UAVs such that it maintains a high-speed communication in remote areas. A swarm or a group of UAVs, on other hand entails a major issue when there exists an internetworking between them. Hence, the routing between the UAVs in the context of 3D intercom poses a design challenge. The Mobility of Unmanned Ariel Vehicle (UAV) enables the internetworking among them to form a cooperative cluster or swarm behavior. A three-dimensional (3D) intercommunication (intercom) is enabled as a preliminary feature in 6G network that supports high-speed communication. The combination of UAV at a greater altitude of various sizes and 3D intercom facility in 6G supports remote connections but at the cost of energy efficiency and high latency. In this paper, we develop a novel network-optimized 3D intercom (NO3DI) model to increase the connectivity between the UAVs with higher energy efficiency. This novel network-optimized model uses an intelligent Q-learning module to establish routing between the UAVs such that a reliable high-speed communication is maintained between the UAVs, which forms a strong intercommunication between the UAVs. Thus, a high-speed non-latent communication is established between the UAVs and the adoption of 6G networking maintains high data rate even in adhoc environments. The simulation is conducted in matlab to test the ability of internetwork among the UAVs under various UAV sizes and the Q-learning routing is tested in terms of packet delivery rate, latency and energy efficiency. The results of simulation shows that the proposed NO3DI achieves reduced energy consumption and maintains a reliable data rate with a high-speed packet delivery rate. Also, the latency is highly reduced as the Q-learning effectively routes the data packets by maintaining a high-speed communication between the UAVs. © 2023

关键词： Queueing networks

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CNN-based Plant Leaf Disease Detection: A Key Solution for Enhancing Agricultural Productivity 3

CNN-based Plant Leaf Disease Detection: A Key Solution for E...

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3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Nayak, Deepika Nair, Rekha R Tinababu Kishore, S. Alliance University Department of Computer Science and Engineering Alliance School of Advanced Computing Bengaluru India Department of Computer Applications Karnataka Mangaluru India

ISBN: (纸本)9798350354171

Agriculture plays a pivotal role in our lives and holds significant importance in our economy. Proper management of agricultural practices is essential for maximizing profits in agricultural production. However, many farmers lack expertise in identifying and managing plant leaf diseases, leading to reduced crop yields. Since agricultural productivity directly impacts the profitability of farming, efficient disease detection and management are crucial. To address this issue, Convolutional Neural Networks (CNN) emerge as a viable solution for leaf disease detection and classification. The primary objective of this research is to develop a robust CNN-based system capable of detecting and classifying leaf diseases in various crops such as apple, grape, corn, potato, tomato, and more. In this research, the CNN algorithm was utilised, and the accuracy was 97.58%. The system's application will enable farmers to monitor large fields of crops, facilitating early detection and treatment of diseases. The significance of plant leaf disease detection spans across multiple sectors, including Biological Research and Agriculture Institutes. Detecting diseases promptly can help implement timely medical treatments, thereby mitigating the negative impacts on crop health and productivity. Moreover, such a system can assist in monitoring crop health on a larger scale, benefiting the agricultural sector as a whole. © 2024 IEEE.

关键词： Agribusiness

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