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A Diagnosis Model Based on Federated Learning for Lung Cancer Classification 2nd

A Diagnosis Model Based on Federated Learning for Lung Cance...

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2nd International Conference on science, engineering Management and Information technology, SEMIT 2023

作者： Babu, Ann Mary Bellamkonda, Sivaiah Computer Science and Engineering Department Indian Institute of Information Technology Kerala Kottayam India

ISBN: (纸本)9783031722837

Lung cancer, causing a significant number of fatalities annually, is a prominent contributor to global mortality rates among individuals of both genders, with an estimated five million cases resulting in death each year. The utilization of Computed Tomography (CT) scans contribute valuable data for the detection of lung cancer. The main objectives of this study are to diagnose malignant lung nodules in the provided input lung images and to classify lung nodule based on its level of severity. This study proposes a solution wherein locally computed updates are combined to stimulate the learning of a shared model, while the training data remains dispersed throughout mobile devices. Federated Learning (FL) is an advanced technology that operates in a collaborative and decentralized manner, with the primary objective of preserving privacy. Its purpose is to address the obstacles posed by data silos and the sensitivity of data. Therefore, the FL approach is employed for the classification of medical images in collaborative and real-world circumstances. A new approach is proposed in this paper, where the model is trained on data distributed across multiple institutions, and the training process occurs locally at each institution. The performance of the proposed method in terms of accuracy, specificity and sensitivity is evaluated and compared with that of the existing models such as Googlenet, Alexnet, and SVM. The proposed method performs comparably better in all three performance measures indicating that it works better than other methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Lung cancer

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Prediction Using Sentiment Analysis on Multi-domain Category: Stocks and Politics Using Twitter Repository 8th

Prediction Using Sentiment Analysis on Multi-domain Category...

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8th International Conference on computers, Management and Mathematical sciences, ICCM 2022

作者： Sanjana, G. Monika Kumar, I. L. V. Sai Nymisha, S. Gowtham, R. Sai Nutipalli, Preeti Department of Computer Science Engineering Anil Neerukonda Institute of Technology and Sciences Visakhapatnam India

ISBN: (纸本)9783031511660

Twitter is a microblogging website and a large public database of various people, products, services, and businesses. The process of analyzing one’s reputation in the public eye is called sentiment analysis. The system should be able to analyze the text, including any special characters like emojis, abbreviations, etc., and classify the user’s opinion as negative, positive, or neutral. It should also present visual statistical analysis (graph) for predicting the likely outcome of the event, given the various opinions of individual users on a particular topic (political affairs, stock prices, etc.). This page focuses on various methods used to analyze product reviews, political pundits, and the general public (who can use the tweet format) to determine whether there are many positive, negative, and neutral opinions that help in product, economy, political results, current events, etc. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Forecasting

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Optimizing IoT Workloads for Minimal Time and Energy with UAV Networks Using Deep Q Network 18

Optimizing IoT Workloads for Minimal Time and Energy with UA...

引用

18th IEEE International Conference on Vehicular Electronics and Safety, ICVES 2024

作者： Singhal, Priyansh Chauhan, Harshit Hazra, Abhishek Joshi, Piyush Indian Institute of Information Technology Sri City Department of Computer Science and Engineering India

ISBN: (纸本)9798350387278

The rapid growth in the use of IoT devices has highlighted significant challenges due to their limited computational power and battery life, often resulting in long task execution times and potential battery depletion. The problem we address is the inefficiency in task completion and high battery usage in IoT devices. Our key objective is to minimize time delays and energy consumption in IoT workloads. To solve this problem, we propose a partial offloading algorithm for IoT devices, utilizing Unmanned Aerial Vehicles (UAVs) due to their larger battery capacity and greater computational power. UAVs provide flexibility in task management by moving to high-demand areas. Given the battery constraints of UAVs, a network of UAVs is used to share tasks among neighboring UAVs, balancing the load. Additionally, tasks can be offloaded to a ground-based cloud server to prevent overloading the UAV network. Our main contribution is the implementation of a Deep Q Network (DQN) reinforcement learning approach to manage the dynamic nature of task distribution. We achieved a significant reduction in both time delays and energy consumption. Our results displayed approximately a 15-fold reduction in total delay and a 6-fold reduction in total energy consumption, demonstrating the effectiveness of DQNs in this task offloading scenario. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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AI-Driven Cryptographic Algorithm Identification: Exploring Methodologies and Practical Applications

AI-Driven Cryptographic Algorithm Identification: Exploring ...

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2024 IEEE International Conference on Machine Learning and Applied Network Technologies, ICMLANT 2024

作者： Yadao, Shivani Linh, Nguyen Thi Dieu Fatima, Areesha Puri, Bhuvan Department of Computer Science Stanley College of Engineering and Technology for Women Hyderabad India Department of Science and Technology Hanoi University of Industry Hanoi Viet Nam Department of Computer Science C.T. Institute of Engineering & Management Punjab Jalandhar India

ISBN: (纸本)9798350389180

This paper examines the application of Artificial Intelligence (AI) and Machine Learning (ML) techniques for classifying and detecting cryptographic algorithms in network traffic, particularly as cyber threats and financial scams continue to rise globally. Cryptographic algorithms are fundamental to data security, ensuring confidentiality, integrity, and authentication. The study explores the evolving role of AI in cryptography, focusing on its potential to overcome limitations of traditional detection methods, particularly in addressing novel threats and encrypted data. It analyzes how these advanced approaches can efficiently classify and identify encryption algorithms, potentially revolutionizing cybersecurity practices. The paper discusses practical applications of AI/ML in cryptographic algorithm identification, examining their impact on malicious code detection, real-time threat analysis, and overall cryptographic security enhancement. Additionally, it addresses current challenges in the field and proposes future research directions. Using data extracted from CrossRef, this study provides a comprehensive analysis of current research trends and methodologies in AI-driven cryptographic identification. By leveraging AI’s capacity to analyze large datasets and adapt to unknown algorithms, this approach aims to significantly improve the reliability and efficiency of cybersecurity systems. The study contributes to the evolving landscape of cryptographic security. © 2024 IEEE.

关键词： Cyber attacks

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Exploring Denoising Diffusion Models for Realistic Anime Character Generation 16

Exploring Denoising Diffusion Models for Realistic Anime Cha...

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16th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2024

作者： Kumari, Vishakha Bhadoria, Robin Singh National Institute of Technology Department of Computer Science & Engineering Himachal Pradesh Hamirpur India

ISBN: (纸本)9798331505264

In recent years, many brands have started incorporating anime characters into their marketing strategies to boost brand recognition and appeal to a broader audience. These generated characters can also be utilized in online games, further enhancing creativity within the entertainment industry. However, image generation in style transfer tasks presents significant challenges due to the complex variations seen in anime images. It is crucial to retain key features, such as emotions and gestures, during image generation. Generative models like Denoising Diffusion Probabilistic Models (DDPMs) are effective in producing high-quality and detailed images. They work by progressively refining noisy inputs, making them well-suited for capturing intricate details like emotions and gestures. However, the sampling process in DDPMs requires running a Markov chain through numerous steps, making the process computationally expensive. To address these challenges, Denoising Diffusion Implicit Models (DDIMs) have been introduced. DDIMs generate high-quality samples with improved efficiency by using a more implicit, deterministic method for denoising at each stage, significantly speeding up the image generation process. © 2024 IEEE.

关键词： Markov chains

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License Plate Recognition Using Artificial Neural Network Techniques 6th

License Plate Recognition Using Artificial Neural Network Te...

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6th International Conference on Communications and Cyber-Physical engineering, ICCCE 2023

作者： Aruna, V.S. Ravi, S. Suruthi, M. Department of Computer Science School of Engineering and Technology Pondicherry University Puducherry605014 India

ISBN: (纸本)9789819971367

In this paper, different Artificial Neural Network techniques are analysed in the context of license plate detection and recognition. In addition, the license plate detection and recognition using various publicly and privately available image datasets, pre-processing techniques, feature extraction techniques, comparison between different artificial neural network (ANN) techniques, such as, Back Propagation Neural Networks, Recurrent Neural Network (RNN) and different convolutional neural network (CNN) techniques are discussed. It also correlates the different metrics used for evaluating performance of artificial neural networks. Advantages and the limitations of the Artificial Neural Network techniques are highlighted based on the discussion and analysis. A total of 30 research publications are considered for the time period of 2002–2022. This systematic literature review will be helpful to the budding researchers due to the detailed analysis of different methodologies in conducting further investigations. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Recurrent neural networks

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Single Use Plastic Bottle Recognition and Classification Using Yolo V5 and V8 Architectures 4th

Single Use Plastic Bottle Recognition and Classification Usi...

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4th EAI International Conference on Cognitive Computing and Cyber Physical Systems, IC4S 2023

作者： Matta, Venkata Durgarao Mudunuri, K. A. Venkata Ramana Raju Sai Baba, B. Ch. S. N. L. S. Kiran, Kompella Bhargava Veenadhari, C. H. Lakshmi Prasanthi, B.V. Computer Science and Engineering Department Vishnu Institute of Technology Andhra Pradesh Bhimavaram India

ISBN: (纸本)9783031488900

Improper disposal of single use plastic bottles leads to many problems including danger to marine life and land pollution. Burning of plastic in turn releases dioxins and polychloride biphenyls. They are very harmful if inhaled and are threat to vegetation too. Manual sorting of plastic bottles and safe disposal is not an easy task. A lot of recycling initiatives use manual sorting for plastic recycling, which depends on plant staff visually identifying and selecting plastic bottles as they move along the conveyor belt. Automatic sorting of plastic bottles has advantage of non-intrusive sorting, speed, consistency, cost effectiveness in long run and even prevents health hazards to workers working in recycling environment. As a result, it is imperative to replace human sorting systems with intelligent automated systems. In this study, convolutional neural network architectures such as YOLOv5 and YOLOv8 were utilized to detect plastic bottles in images. Despite YOLOv8 having more parameters and requiring more computation time, it was found that YOLOv8 outperformed YOLOv5 in accurately identifying plastic bottles in the images. © 2024, ICST Institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Cost effectiveness

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Securing Health Monitoring in IoT with Blockchain-based Proxy Re-Encryption 3

Securing Health Monitoring in IoT with Blockchain-based Prox...

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3rd International Conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： George, Jerlin Chitra, R. Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Tamil Nadu India

ISBN: (纸本)9798331532420

The integration of Internet of Things (IoT) devices in healthcare has revolutionized patient monitoring and remote healthcare. However, concerns about data security and privacy persist. This research proposes a novel approach that combines blockchain technology and proxy re-encryption to enhance the security and privacy of IoT-based healthcare systems. By leveraging blockchain's decentralized and immutable nature, the proposed system ensures the integrity and transparency of health data. Proxy re-encryption enables secure and controlled sharing of encrypted data among authorized healthcare providers. This research aims to address the challenges of data security and privacy in IoT-based healthcare, providing a robust and secure platform for remote patient monitoring and data sharing. © 2024 IEEE.

关键词： Electronic health record

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Enhancing URL Security and Access Control Using Hash-Based Shortening Algorithm 2

Enhancing URL Security and Access Control Using Hash-Based S...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Haripriya, K. Preethi, C. Hameed, S. Shahul Pavalarajan, S. Shushmmitha, T. Haripriya, V. PSNA College of Engineering and Technology Department of Computer Science and Business Systems Dindigul India

ISBN: (纸本)9798331534950

Shortening URLs has become an integral part of modern digital communication, as it simplifies long and complex internet addresses, making them more user-friendly. However, conventional URL shortening methods often lack adequate security provisions, leaving shortened links vulnerable to various risks. This project aims to develop a secure URL-shortening service by incorporating a hash-based access control mechanism. By using a hash-based approach, we can significantly enhance the security of shortened URLs. In this method, a unique hash value is generated for each URL, which serves as a link to the shortened URL. The generated hash is highly resistant to decryption, making it difficult for unauthorized users to retrieve the original URL, thereby ensuring that only the intended recipient can access the link. The goal of this project is to develop a protected and secure URL-shortening service that utilizes a hash-based access control mechanism. By generating a unique hash for each shortened URL, the system will enhance both security and user privacy. Furthermore, the implementation of granular access control will be a key factor in maintaining the system's overall security. SHA-256 can be utilized to generate unique, unpredictable shortened URLs by creating secure hash values. Integrating OAuth2 and JWT provides robust access control, enabling seamless user authentication and authorization across multiple systems. Additionally, AES encryption ensures the protection of sensitive data by offering strong encryption, safeguarding both the original URL and user information. © 2024 IEEE.

关键词： Cryptography

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Texture Recognition Using InceptionNeXt-based Texture Perception Network 15

Texture Recognition Using InceptionNeXt-based Texture Percep...

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

作者： Sahoo, Tapan Kumar Mawari, Gaurav National Institute of Technology Warangal Department of Computer Science and Engineering Telangana Hanamkonda India

ISBN: (纸本)9798350370249

Recognizing textures remains challenging in images due to variations in fundamental elements (primates) and characteristics (attributes) caused by spatial context. Existing Convolutional Neural Network (CNN) approaches typically use detailed local descriptions with unordered aggregation for spatial layout inconsistency. However, these methods overlook the inherent structure relationships between primates and the semantic meanings conveyed by attributes, which are crucial for texture. This paper suggests a newfangled approach for texture cognition using Inception NeXt, a deep learning architecture known for its long-range modelling capabilities. Inception NeXt uses large-kernel convolutions to capture broader contextual information within textures. We address the computational efficiency boundaries associated with large kernels by incorporating an Inception-inspired decomposition strategy. This approach decomposes the large kernels into smaller, parallel branches, enhancing efficiency while maintaining performance. Our model extracts features by analyzing the relationships between these primates and attributes within the spatial context of the texture. The Inception NeXt architecture facilitates the capture of long-range dependencies between texture elements, leading to more robust feature representations. Experiments on two challenging texture databases demonstrate the effectiveness of our approach, achieving superior performance in terms of accuracy, robustness, and efficiency compared to traditional CNN-based methods. © 2024 IEEE.

关键词： Deep learning Large-kernel convolutions Robustness Spatial context Texture recognition

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