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Empowering Breast Cancer Detection with AI: A Modified Support Vector Machine Approach for Improved Classification Accuracy 4

Empowering Breast Cancer Detection with AI: A Modified Suppo...

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4th International Conference on Expert Clouds and applications, ICOECA 2024

作者： Palarimath, Suresh Maran, Pyingkodi Thenmozhi, K. Shiny, K.V. Sujatha, T. N.r, Wilfred Blessing College of Computing and Information Sciences University of Technology and Applied Information Technology Department Sciences Salalah Oman Kongu Engineering College Department of Computer Applications Erode India Department of Compute Science Bengaluru India Bharath Institute of Higher Education and Research Department of Computer Science and Engineering Chennai India Sri Krishna College of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India College of Computing and Information Sciences Univesity of Technology and Applied Sciences Information Technology Department Ibri Oman

ISBN: (纸本)9798350385793

Breast cancer poses a significant threat to women's health, being a leading cause of cancer-related mortality among female population. In recent years, machine learning has emerged as a promising approach in medical field, particularly in detection and classification tasks. However, existing algorithms often exhibit suboptimal accuracy, necessitating improved methodologies. This research presents a novel approach using a Modified Support Vector Machine (MSVM) for breast cancer classification into benign and malignant categories. Leveraging the Wisconsin Breast Cancer Dataset (WBCD), preprocessing techniques are applied to enhance data quality. Principle Component Analysis (PCA) reduces dimensionality, while linear Discriminant Analysis (LDA) extracts discriminative features crucial for classification. The proposed MSVM classifier achieves exceptional performance, with a classification accuracy of 99.42%, outperforming other existing methods such as Deep Convolutional Neural Networks and Fuzzy Rule-based Systems. These results highlight the efficacy of the MSVM approach in accurately distinguishing between benign and malignant breast cancer cases, showcasing its potential as a reliable tool for medical image analysis and cancer diagnosis. © 2024 IEEE.

关键词： Convolutional neural networks

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Ensemble-Based Big Data Analytics for Disease Prediction in Iot 1

Ensemble-Based Big Data Analytics for Disease Prediction in ...

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1st International Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Kumar, Rakesh Madan, Parul Shrivastava, Anurag Kumar, C Praveen Rao, A.L.N. Sankhyan, Akhil Gla University Department of Computer Engineering & Applications Mathura India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India Institute of Aeronautical Engineering Department of Computer Science and Engineering Telangana Hyderabad India Lloyd Institute of Engineering and Technology Greater Noida India Lloyd Law College Greater Noida India

ISBN: (纸本)9798350330915

The developing nexus between Internet of Things (IoT) technologies, healthcare, as well as analytics for prediction is the subject of this study. A deductive method is used to investigate how IoT-generated medical data and group learning approaches might be combined for predicting diseases. The study is guided by a descriptive research approach that emphasizes the necessity to thoroughly evaluate any possible overlap between these categories. This investigation's foundation is secondary data collecting from several IoT-enabled healthcare device sources. Studying the effects of picking features overall engineering, researchers found that doing so significantly increased the accuracy of models by 7% and precision by 8%. The evident superiority using ensemble methods is demonstrated by comparison with conventional models, having Random Forest surpassing logarithms approximately 15% in frequency. The study also assesses each model's efficiency of resources and scaling, emphasizing how practically applicable they are to high-throughput, practical applications. The results demonstrate the intriguing possibility of ensemble-based informatics for predicting illnesses using data provided by the Internet of Things. The report makes recommendations for further research, such as investigating cutting-edge ensemble approaches and incorporating other data modalities. To further improve and validate the suggested approach, undertaking real-world validation tests and incorporating methods for interpretation are advised. The ultimate goal of this research is to transform illness management overall patient outcomes by advancing medical predictive analytics. © 2023 IEEE.

关键词： complicated interdependencies Internet of Things machine learning medical systems untapped market

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Evasion Techniques in Cybersecurity: An In-Depth Analysis

Evasion Techniques in Cybersecurity: An In-Depth Analysis

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2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor applications, ICAIQSA 2024

作者： Turukmane, Anil V Khekare, Ganesh Shelke, Nilesh Sakarkar, Gopal Buchade, Siddhant School of Computer Science & Engineering Vellore Institute of Technology-AP Amaravati India Symbiosis Institute of Technology Nagpur Campus Symbiosis International Deemed University Pune India Dr. Vishwanath Karad MIT World Peace University Department of Computer Science and Applications Pune India

ISBN: (纸本)9798331517953

Because of the rising sophistication of cybersecurity threats, it is imperative to have a thorough grasp of the evasion strategies used by malevolent actors. This study offers a detailed analysis of evasion strategies in several cybersecurity sectors, such as intrusion detection systems, network security, and endpoint protection. This paper employs a methodical approach to examine the development of these methods, showcases actual case studies, and suggests approaches for detection and mitigation. The findings emphasize how crucial it is to conduct ongoing research and development to combat new cyber threats. Key discoveries are reviewed, and directions for further research are indicated. © 2024 IEEE.

关键词： Network intrusion

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Kohonen Self-Organizable Maps based Classification of Optical Code Division Multiple Access Codes 6

Kohonen Self-Organizable Maps based Classification of Optica...

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6th International Conference on Inventive Computation Technologies, ICICT 2023

作者： Shankari, S. Hariharan, G. Kushwaha, Sumit Vidhya, K. Vijaya Kumar Reddy, R. Madan, Parul Department of Computer Science and Engineering R.M.K College of Engineering and Technology Tamil Nadu Puduvoyal601206 India Department of Artificial Intelligence and Machine Learning Malla Reddy University Telangana Hyderabad500043 India Department of Computer Applications University Institute of Computing Chandigarh University Punjab 140413 India Department of Computer Science and Engineering KPR Institute of Engineering and Technology Tamil Nadu Arasur641407 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522502 India Department of Computer Science and Engineering Graphic Era Deemed to be University Uttarakhand Dehradun248002 India

ISBN: (纸本)9798350398496

Passive Optical Networks (PONs) are a type of fiber-optic network that use passive components, such as splitters and combiners, to distribute optical signals to multiple end-users. PONs typically consist of an Optical Line Terminal (OLT), and Optical Network Units (ONUs) or Optical Network Terminals (ONTs). PONs is also more energy-efficient and environmentally friendly than traditional networks, as they use passive components that do not require power. Overall, PONs is a cost-effective and efficient way to deliver high-speed broadband services to end-users. They offer several advantages over traditional networks and are expected to play a critical role in the deployment of next-generation broadband services. Optical code division multiple access (OCDMA) is an important candidate for implementing access control in PONs because it provides related features such as higher security and flexible on-demand bandwidth. In this context, this article presents a new method for classifying OCDMA codes using artificial neural networks, in particular Kohonen self-organizing maps (S OM), which is important for higher security of the network management system. for users, improving the quality of service (QoS). The code classification success rate was 87.5%, which justifies pattern identification methods to improve the optical code detection process. © 2023 IEEE.

关键词： Passive optical networks

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Distributed Deep Learning for Medical Image Processing in Cloud Environments 1

Distributed Deep Learning for Medical Image Processing in Cl...

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1st International Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Varshney, Neeraj Madan, Parul Shrivastava, Anurag Kumar, C Praveen Rao, A Kakoli Srivastava, Amit Gla University Department of Computer Engineering and Applications Mathura India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India Institute of Aeronautical Engineering Department of Computer Science and Engineering Telangana Hyderabad India Lloyd Institute of Engineering and Technology Greater Noida India Lloyd Law College Greater Noida India

ISBN: (纸本)9798350330915

The incorporation of distributed deep learning for medical image processing in cloud settings is the subject of this study. The findings demonstrate the high viability and significant performance advantages realized by cloud-based distributed systems, notably significant processing time savings, outstanding diagnostic accuracy, as well as improved scalability. The consequences for security and privacy have been discussed, with a focus on effective safeguards for private medical information. There is a void in the literature about resource and cost-effectiveness optimization tactics used in cloud-based systems. Future research must concentrate on resource optimization tactics for economic sustainability, study developing security risks and privacy techniques, and incorporate real-world implementations in order to improve this topic. This study informs the use of distributed deep learning in cloud-based medical image processing as well as adds to the body of knowledge in healthcare technology. © 2023 IEEE.

关键词： cloud computing Distributed deep learning medical image processing privacy resource optimization security

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An Overview of Video Tampering Detection Techniques: State-of-the-Art and Future Directions

An Overview of Video Tampering Detection Techniques: State-o...

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2023 International Conference on Computational Intelligence and Sustainable engineering Solution, CISES 2023

作者： Pandey, Saroj Kumar Kumar, Lalan Kumar, Gaurav Kumar, Ankit Singh, Kamred Udham Singh, Teekam Gla University Department of Computer Engineering & Applications UP Mathura India Gl Bajaj Institute of Technology and Management Department of Master of Computer Application India School of Computing Graphic Era Hill University Dehradun India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350323917

In the realm of video processing and forensics, finding video tampering is a critical challenge. The purposeful change or manipulation of video material to conceal or falsely depict the original information is referred to as 'video tampering.' A significant demand for efficient video tampering detection methods has emerged in recent years as a result of the rise in false news, cyberbullying, and other types of criminal behavior. Modern approaches for detecting video manipulation may be generally divided into two categories: passive methods and aggressive methods. Whereas active methods entail putting digital watermarks or other identifiers on the video clip to aid tampering detection, passive methods rely on evaluating the video content for inconsistencies or artifacts generated during tampering. Techniques like sensor noise analysis, splicing detection, and forgery detection based on compression artifacts are examples of passive approaches. To find any discrepancies produced by manipulation, sensor noise analysis examines the noise patterns in the video. Splicing detection entails identifying the borders between several video segments and inspecting the content of each segment for irregularities. Analyzing the compressed video stream for irregularities caused during tampering is required for detecting forgeries based on compression artifacts. Techniques like digital watermarking, digital signatures, and fingerprinting are examples of active procedures. To detect any manipulation efforts, digital watermarking entails putting a digital watermark into the video clip. To confirm the legitimacy of the video clip, a digital signature is applied to it. To do fingerprinting, special characteristics from the video footage are extracted and saved in a database. In addition to the aforementioned methods, machine learning-based systems have also demonstrated promising outcomes in the identification of video manipulation. Using these methods, any tampering attempts in fresh mov

关键词： Authentication

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Advances in Copy Move Forgery Detection in Digital Images: A Comparative Examination of Conventional Approaches and Deep Learning Based Models

Advances in Copy Move Forgery Detection in Digital Images: A...

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2023 Annual International Conference on Emerging Research Areas: International Conference on Intelligent Systems, AICERA/ICIS 2023

作者： Krishnalal, G. Jagathy Raj, V.P. Madhu, G. Arun, K.S. School of Engineering Cochin University of Science & Technology Cochin India School of Management Studies Cochin University of Science & Technology Cochin India Cochin University of Science & Technology Department of Computer Applications Cochin India

ISBN: (纸本)9798350303452

In recent years, the ease of digital image alteration has significantly increased, primarily driven by the advancements in computing technology and the availability of user-friendly professional image editing software. Consequently, the process of authenticating digital images has become increasingly complex, especially when attempting to distinguish between altered and genuine images with the naked eye. This paper aims to explore various techniques used to detect Copy-Move Forgery in digital images. It provides a comparative analysis of conventional approaches, such as Binary Discriminative Features with Mean Colour Similarity, Color Histogram, DCT and SIFT against the deep learning models AlexNet and GoogleNet. Benchmark datasets such as MICC-F2000 and CoMoFoD are used to evaluate the performance of the comparing models. While conventional techniques exhibit respectable precision, recall, and F1 scores, contemporary deep learning methods outperform them marginally. © 2023 IEEE.

关键词： Deep neural networks

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IoT Based Human Biometric System Using Alcohol Level Detection for Driver Security 1

IoT Based Human Biometric System Using Alcohol Level Detecti...

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1st International Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Sinha, Anurag Kumar, Nitish Ticku, Amrita Modi, Vyom Shah, Meghal Shrivastava, Kapil School of Computing and Information Sciences Ignou New Delhi India Lovely Professional University Department of Computer Science Punjab Phagwara144001 India Bharti Vidyapeeth's College of Engineering India India Faculty of Engineering Marwadi Education Foundation Gujarat Rajkot360006 India Charotar University of Science & Technology Department of Computer Science & Engineering Gujarat 388421 India Department of Computer Engineering and Applications GLA University Mathura India

ISBN: (纸本)9798350330915

In today's society, driving after intoxication has become a major factor in traffic accidents. It is common knowledge that operating a vehicle while intoxicated is a major contributing factor in a significant number of accidents. The effects of drunk driving go beyond the drunk driver;they also affect other drivers and passengers in the same vehicle. As a result, this growing threat needs to be addressed immediately. Our project's goal is to create a system that uses the Internet of Things (IoT) to find traces of alcohol in someone's breath and apply preventive measures in order to effectively address this issue. Although there are laws against drunk driving, it is difficult to enforce them widely because law enforcement cannot constantly be present on the roads to spot drunk drivers. This restriction increases the chance of accidents significantly. In order to reduce future risks, a trustworthy system that can recognize and discourage drunk drivers is urgently needed. Drunk driving deaths are increasing, so we need to figure out how to reduce these kinds of deaths. The breath analyzing sensor used in the suggested system is the MQ-3, which calculates the breath alcohol content of an individual. The amount of alcohol that is allowed in the blood in India is 0.03. The engine won't start if the person's blood alcohol content is higher than this limit, making it impossible for them to drive. Additionally, the system will send an alert, possibly through a Raspberry Pi, to a friend or relative, providing the exact location using GPS coordinates. This notification serves as a precautionary measure to keep others informed about the situation. Implementing such a system can contribute significantly to reducing accidents related to alcohol consumption. In conclusion, detectors like the one proposed in this project hold immense significance in preventing the loss of life and property caused by reckless driving under the influence of alcohol. © 2023 IEEE.

关键词： Internet of things

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Temporal Dynamics of Consumer Engagement in E-Commerce

Temporal Dynamics of Consumer Engagement in E-Commerce

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Computing for Sustainability and Intelligent Future (COMP-SIF), International Conference on

作者： Gomathi Thiyagarajan Swathi. Y Department of Computer Applications CMR Institute of Technology Bengaluru India Department of Computer Science and Engineering Gopalan College of Engineering and Management Bengaluru India

ISBN: (数字)9798331538538

ISBN: (纸本)9798331538545

This paper explores into an indepth analysis of consumer behavior on an e-commerce platform, extending the study period to include November 2019 through February 2020. Looking at user interactions that come under the categories of views, carts, purchases, and removals from cart along with process discovery visualizations will help identify complex patterns of engagement, measure the impact of interface changes and quantify marketing intervention's successes. This research uses sophisticated data analytics through process mining to provide recommendations for improving the e-commerce journey through increased user satisfaction and better conversion rates.

关键词： Process mining Data analysis Consumer behavior Data visualization Electronic commerce Sustainable development

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Detection of Tuberculosis using a Multi-model Classification Approach on CXR Images

Detection of Tuberculosis using a Multi-model Classification...

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2022 International Conference on Artificial Intelligence and Data engineering, AIDE 2022

作者： Rajesh Babu, Tina Nair, Rekha R. Pechetti, Punith School of Engineering Dayananda Sagar University Department of Computer Science and Engineering Bengaluru India School of Engineering Dayananda Sagar University Department of Computer Applications Bengaluru India Bengaluru India

ISBN: (纸本)9781665493048

Tuberculosis is known as one of the most dangerous infectious diseases in the world. TB is treatable with proper diagnosis and preventive care. They are known as an' air-borne disease,' as they spread through sneezes and coughs. Usually, sarcoidosis, pneumonia, and lung cancer are misdiagnosed as TB or vice versa. TB diagnosis can be done with chest X-rays, sputum tests, blood tests, and the tuberculin skin test (TST). Generally, in clinical practice, CXRs are used by radiologists for the detection of TB. But in low-resource countries, there is also a lack of trained radiologists, which leads to improper medication and an increased fatality rate. To overcome this, many models have been developed with the help of machine learning and deep learning to automate the detection of tuberculosis using CXRs. The dataset used is a combination of two publicly available datasets: the Montgomery dataset and the Shenzhen dataset. This research is based on experimental processes such as resizing, filtering, feature extraction, and several classification algorithms that include both ML and DL. Then more than one classification algorithm is used to compare the model outcome with their respective accuracies. The algorithms with higher accuracies were combined together to perform ensemble technique. This enhances the accuracy that will be used to develop a model in the future. © 2022 IEEE.

关键词： Diagnosis

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