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Lung Cancer Image Identify and Categorization using Artificial Neural Networks

Lung Cancer Image Identify and Categorization using Artifici...

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2024 International Conference on Computational Intelligence for Green and Sustainable Technologies, ICCIGST 2024

作者： Lingappa, Ediga Begum, D Jareena Pachouri, Priya Rashmi, Kuderu Varalakshmi, Thokala Tharuna Kumar, Anoop Mallareddy College of Engineering Maisammaguda Dept of Computer Science and Engineering TS Secunderabad500100 India CMR Institute of Technology Dept. of Artificial Intelligence and Data Science Bengaluru560037 India Malla Reddy University Dept of Cyber Security Hyderabad India

ISBN: (纸本)9798350381887

Lung cancer that is made up of small cells is called small cell lung cancer (SCLC). Smoking tobacco in any form-cigarettes, cigars, or pipes-is by far the most significant risk factor for developing lung cancer, though it is only one of many. Cancer of the lungs is known as lung cancer. When cells in the body multiply uncontrollably, cancer develops. If cancer cells enter the lymphatic system or the bloodstream, they can travel to other organs and spread the disease. Cheap and fast cancer diagnostics could save millions. Latestage cancer treatment is hard. Lung cancer is diagnosed non-invasively. Biopsy requires careful ANN architecture selection. This study proposes a diagnosis ANN model. ANN research is 'hot' in cardiology, cancer, and radiology. Medical research uses ANNs. Lung cancer diagnosis was tested. MR images assess lung health for the neural network. MATLAB created ANNs and processed images. Training data was extracted from MR images using Harlick texture features. MATLAB made ANNs. Elman Network exceeds ANNs with 90.30 %. Lung cancer can develop. MRIs detect lung cancer. MRIs show lung development. Many lung tumour detection research use machine learning and deep learning. MRI algorithms swiftly detect lung malignancies, improving patient therapy. Radiologists use these forecasts. The study evaluates a self-defined ANN for lung tumour identification. The radiologist needs time to find and remove the cancer from the MR. Technology improves performance and segmentation. Machines always predict and detect. Neural networks outperformed SVM, Random forest, regression, and others. © 2024 IEEE.

关键词： Lung cancer

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Blockchain-Based Music Streaming Services: A Novel Approach to Transparent Artist Control 3

Blockchain-Based Music Streaming Services: A Novel Approach ...

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3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Ashtagi, Rashmi Deshmukh, Tanmay Jadhav, Swati Bidwe, Ranjeet Akkalkot, Afsha Buchade, Amar Uke, Nilesh Banerjee, Sudipta Vishwakarma Institute of Technology Dept. of Computer Engineering Pune India Symbiosis Institute of Technology Dept. of Cse Pune India Coep Technological University Dept. Name of Computer Engineering Pune India Data Science Vishwakarma Institute Technology Dept. of Artificial Intelligence Pune India Indira College of Engineering and Management Pune India Symbiosis Institute of Technology Pune India

ISBN: (纸本)9798350354348

Artists have historically used music as a form of personal expression. However, due to the centralized nature of music streaming services, artists receive only a small percentage of the revenue generated and have no control over their content produced. To address these issues, a revolutionary technique is needed that provides artists with greater control and transparency over their output. This paper focuses on a decentralized strategy that utilizes blockchain technology to facilitate direct interaction between artists and users, enabling artists to upload their music, sell songs, and receive royalties. The implementation of blockchain technology could potentially revolutionize the music industry by ensuring that rightful owners receive payment for their music while reducing the waste of paying intermediaries. Without intermediaries involved in the distribution process, the music industry could pay out more royalties to rights holders automatically and instantaneously. The proposed solution also addresses piracy concerns through the use of encryption and watermarking techniques. Blockchains provide security, confidentiality, tamper-proofing, traceability, control, and oversight over information. By developing a secure, self-installing, cross-platform reader positioned on the header of the digital content file, The research empowers data owners to manage the flow of information even after it has been made available. The suggested approach enables content owners to control their digital content and monitor its most popular components after distribution. The blockchain-based system also benefits from token distribution among the content's owner, distributor, and consumer. The simulation results demonstrate that our approach is capable of overcoming the flaws of the existing system. © 2024 IEEE.

关键词： Blockchain

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Ethereum Blockchain-Based Design and Implementation of Electronic Records for Home Care 2

Ethereum Blockchain-Based Design and Implementation of Elect...

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Jahir Pasha, M. Baseer, K.K. Vasavi, N. Sreenivasulu, K. Mohammed Nadeem, Syed Obula Reddy, A. Chandra Andhra Pradesh Nandyal India Gitam School of Technology Karnataka Bengaluru India Ashoka Womens Engineering College Dept. of CSE Kurnool India G. Pullaiah College of Engineering and Technology Dept. of CSE Kurnool India Dept. of CSE-Data Science Kurnool India Kadapa India

ISBN: (纸本)9798350365269

Blockchain's distributed and decentralized ledger, which conducts transactions over multiple computers and is digitally encrypted, will play a significant role in the future. It is meant to be transparent, impervious to tampering, and censorship-resistant. Building safe blocks joined by a chain containing transactions and a hash value is the fundamental idea underlying blockchain. Cryptographic hashes are used to join these blocks together to create an unchangeable, continuous chain. Many elements of the Ethereum blockchain can be useful when utilizing smart contracts. Thus, we make use of these smart contracts along with the many blockchain components that would be highly beneficial on the Ethereum network, and we thoroughly examine the transaction flow. Beyond only cryptocurrencies, Ethereum has been essential in the advancement of blockchain technology, opening the door for a wide range of decentralized projects and applications. In homecare, the low-secured records are utilized for family member and doctor consultations as well as the storage of diagnosis information. The majority of hospitals utilize email to share critical medical details due to its lower security and reliance on data. Most people are most familiar with blockchain technology as the foundation of cryptocurrencies like Bitcoin, where it serves as a safe and decentralized ledger for recording transactions. Its possibilities, however, go beyond cryptocurrency to a variety of other areas, such as banking and healthcare, where it might enable new kinds of decentralized apps and offer transparent and safe record-keeping. A blockchain system is an application code for a decentralized blockchain platform that runs and validates securely via a block-to-block network. Keeping a lot of data on the blockchain costs a lot of money, and exchanging health records is one issue that needs to be addressed. The recommended method for resolving this issue employs the Interplanetary File System (IPFS) to save costs

关键词： Smart contract

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Improving Time-Series Classification Accuracy Based on Temporal Feature Representation Learning Using CRU-LSTM Autoencoder 5

Improving Time-Series Classification Accuracy Based on Tempo...

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5th IEEE International Conference on Cognitive Machine Intelligence, CogMI 2023

作者： Kim, Dohee Sim, Sunghyun Yoon, Bori Liu, Ling Bae, Hyerim Pusan National University Major in Industrial Data Science & Engineering Dept. Industrial Engineering Busan Korea Republic of Dong-Eui University Dept. Industrial Management & Big Data Engineering Busan Korea Republic of College of Computing Georgia Institute of Technology Georgia United States

ISBN: (纸本)9798350323832

Time-series data consists of a sequence of observations recorded in chronological order, where the data changes over time. This type of data exhibits various characteristics, such as temporal volatility, trends, and seasonality. Recently, a new layer structure called Correlation Recurrent Units (CRU) has been proposed to capture not only temporal variability but also trends and seasonality in time-series data. In this study, we propose an end-to-end model that utilizes a CRU autoencoder to learn temporal feature representations and address time-series classification problems simultaneously. To validate the performance of our proposed model, we conducted comparative experiments using 30 time-series classification datasets from six different types. The experimental results showed that the proposed model outperformed the baseline models in 20 out of the 30 time-series datasets. This indicates that the proposed approach effectively captures various temporal features in time-series data and improves the performance of time-series classification tasks. © 2023 IEEE.

关键词： Time series

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Medication Management Application for Directly Observed Treatment of TB Patients 6

Medication Management Application for Directly Observed Trea...

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

作者： Tania, Marzia Hoque Rahman, Md. Asib Ahmed, Kadir Uddin Centre for Big Data Research in Health Unsw Sydney Sydney Australia Buet Dept. Computer Science and Engineering Dhaka Bangladesh Independent Researcher Sydney Australia

ISBN: (纸本)9798350385779

Tuberculosis (TB) poses global health challenges, requiring efficient management and monitoring of patients undergoing Directly Observed Therapy (DOT). This paper introduces TB-meds, a proposed software application to automate essential aspects of TB patient management. TB-meds aims to optimize resource utilization, improve patient care, and enhance TB control efforts. The application streamlines medication distribution, facilitates medication adherence monitoring, sends timely clinical test requirement's notifications, and enables convenient treatment status updates. By automating these processes, TB-meds has the potential to enhance efficiency, improve treatment outcomes, and reduce transmission rates. Its user-friendly interface promotes seamless communication among healthcare providers and TB patients. With tasks automation, the workload of DOT providers can be reduced, and more time can be dedicated to patient education, nutrition counseling, and lifestyle guidance. Additionally, TB-meds proposes to revolutionize TB surveillance by enhancing data collection and analysis capabilities, enabling informed decision-making and targeted interventions. The implementation of TB-meds holds promise for transforming TB patient management, contributing to better health outcomes, and advancing TB control efforts. © 2024 IEEE.

关键词： Telemedicine

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Deep Neural Networks in Social Media Forensics: Unveiling Suspicious Patterns and Advancing Investigations on Twitter 3

Deep Neural Networks in Social Media Forensics: Unveiling Su...

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3rd Intelligent Cybersecurity Conference, ICSC 2023

作者： Sharrab, Yousef Al-Fraihat, Dimah Alsmirat, Mohammad Isra University Dept. of Data Science and Artificial Intelligence Amman Jordan Isra University Dept.of Software Engineering Amman Jordan University of Sharjah Dept. of Computer Science Sharjah United Arab Emirates

ISBN: (纸本)9798350382174

Text data forensics, a rapidly developing field that focuses on analyzing textual content to identify criminal or suspicious activities, is becoming increasingly important due to the popularity and the huge number of text posts on social media platforms. This study aims to improve the detection of suspicious text on social media using deep neural networks. Suspicious text is defined as any text that is likely to be associated with criminal activity or is unusual or out of the ordinary. This study could make a significant contribution to the field of text data forensics by helping to improve the detection of such text. We leveraged the 'CIC Truth Seeker dataset 2023' [1], which is widely recognized as a comprehensive and representative dataset for text data forensics research. The dataset contains over 180,000 tweets related to 700 real and 700 fake pieces of news, labeled by experts. In this study, we enhance text data forensics in social media by leveraging the powerful analytical capabilities of deep neural networks. More specifically, we investigate the effectiveness of Long Short-Term Memory (LSTM) in the detection of suspicious text. The results are very promising as we achieved an accuracy of 96% during preliminary evaluations. We plan to explore future work on the model's potential applications, including criminal activity identification, misinformation detection, and online harassment prevention. © 2023 IEEE.

关键词： Sentiment analysis

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Diversity Seeking Techniques for Red-Teaming Large Language Models

Diversity Seeking Techniques for Red-Teaming Large Language ...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Lee, Seokhan Ku, Bonhwa Ko, Hanseok Dept. Computer Science and Engineering Korea University Seoul Korea Republic of Machine Learning Big Data Institute Korea University Seoul Korea Republic of Dept. Electrical and Computer Engineering Korea University Seoul Korea Republic of

ISBN: (纸本)9798350368741

In this paper, we present new techniques for increasing the diversity of red-teaming prompts generated by automated machine learning-based methods, thereby enabling the discovery of more vulnerabilities in large language models. Using reinforcement learning to train models to output effective prompts for this task results in the models converging deterministically to a single output. Our first technique, which we term Defender, acts by blocking the reward signal for prompts that have already been discovered, thus making what was a stationary problem into a non-stationary problem that compels the reward maximizing algorithm to continually seek new prompts. Our second technique, Teamplay, trains two prompt generation models in tandem and adds the KL divergence between them to the reward in order to make them search in disparate regions of the space of prompts. Our techniques are shown experimentally to increase the effectiveness and diversity of prompts generated by existing reinforcement learning baselines. © 2025 IEEE.

关键词： AI safety large language models red-teaming reinforcement learning toxicity

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Evaluation of Road Network Effectiveness with Rank-Based Random Walk 18

Evaluation of Road Network Effectiveness with Rank-Based Ran...

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18th International Conference on Ubiquitous Information Management and Communication, IMCOM 2024

作者： Lee, Da-Young Cho, Hwan-Gue Pusan National University Information Convergence Engineering Dept. Busan Korea Republic of Graduate School of Data Science Pusan National University Busan Korea Republic of

ISBN: (纸本)9798350331011

Research into efficient routing algorithms within general graph networks has been extensively conducted. However, there has been limited exploration regarding the effectiveness of road networks when specific routing techniques are applied, and the characteristics of real road networks. Although routing characteristics have been theoretically and experimentally well-established for geometric graph types like the Delaunay graph, it is important to note that these differ significantly from real-world road networks. In response to this gap in research, we introduce a novel rank-based random walk model that integrates deterministic online routing and random walk. Our evaluation of road network effectiveness is grounded in the success rates observed when applying online routing to actual road networks. Additionally, we provide specific conditions for ensuring favorable success rates within each road network, along with appropriate routing strategies. This approach allows us to effectively characterize road networks. © 2024 IEEE.

关键词： Autonomous vehicles

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Detection of Geometry-based Adversarial Facial Attacks using Error Level Analysis

Detection of Geometry-based Adversarial Facial Attacks using...

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2022 IEEE Region 10 International Conference, TENCON 2022

作者： Sadu, Chiranjeevi Das, Pradip K. Rajiv Gandhi University of Knowledge Technologies Dept. of Computer Science and Engineering Nuzvid India Indian Institute of Technology Guwahati Dept. of Computer Science and Engineering Guwahati India

ISBN: (数字)9781665450959

ISBN: (纸本)9781665450959

Despite Machine Learning (ML) and Deep Learning (DL) models in specific have achieved significant success in various applications of computer vision in recent years, they remain vulnerable to carefully crafted, human-invisible adversarial attacks. Adversarial attacks are generated intentionally by adding small perturbations to an input image to mislead the classifier. This paper investigates a new defense method against geometry-based adversarial face attacks that significantly mitigate the impact of such adversarial attacks on image-classification models. We generate geometry-based adversarial face images based on Fast Landmark Manipulation (FLM) method and Error Level Analysis (ELA) is performed to effectively discriminate adversarial face images from clean images. The performance of the proposed defense method is demonstrated on a real-world dataset with different types of classifiers. The experimental results show that our defense method can effectively detect adversarial face images from clean images with 99.75% accuracy. © 2022 IEEE.

关键词： Geometry

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Deep Learning Driven - Pest Classification and Sustainable Agriculture 3

Deep Learning Driven - Pest Classification and Sustainable A...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Kumar, M. Sirish Usha, R. Reddy, B. Rama Kantha Ramachandra, S. Prakash, P. Jaya Swetha, B. Mohan Babu University School of Computing Dept. of Data Science India Madanapalle Institute of Technology and Science Madanapalle India Sri Venkateswara College of Engineering Tirupati India Mother Theresa Institute of Engineering and Technology Palamaner India Sri Venkateswara College of Engineering Department of It Tirupati India Mohan Babu University Dept. of Master of Computer Applications India

ISBN: (纸本)9798331509675

This study introduces a system designed to identify pests in crops and classify them as either beneficial or harmful. The project begins by providing a comprehensive overview of various pest identification methods, analyzing their strengths and limitations. To address the problem, a categorization approach is proposed that incorporates convolutional neural networks (CNN) to build the pest identification and classification model. The model was trained using a dataset containing images of different pest species. Extensive testing was conducted on a large dataset, and the system's performance was validated against multiple traditional classification models. The proposed framework achieved a classification accuracy of 90%, outperforming conventional methods. This research highlights agriculture's vital role in the economy and underscores the potential of advanced technologies in supporting sustainable farming practices. © 2025 IEEE.

关键词： Agriculture Sustainability Classification Accuracy Classification Model Convolutional Neural Network (CNN) Pest Classification

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