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Federated Learning in Healthcare: A Privacy-Preserving Approach to Predictive Analytics

Federated Learning in Healthcare: A Privacy-Preserving Appro...

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2025 IEEE International Conference on Computational, communication and Information Technology, ICCCIT 2025

作者： Reddy, M. Jithender Kotiyal, Arnav Lakhanpal, Sorabh Badhoutiya, Arti Mounika, T. Jayanthi, Muthuswamy Vasavi College of Engineering Dept. of Computer Science and Engineering Telangana Hyderabad India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Lovely Professional University Punjab Phagwara India GLA University Department of Electrical Engineering Mathura India MLR Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India New Horizon College of Engineering Department of Electronics and Communication Engineering Bangalore India

ISBN: (纸本)9798331512965

Technology such as machine learning in healthcare has pushed privacy concerns to the point that the handling of sensitive patient data has become paramount. In the context of FL, predictive analytics without data sharing between institutions can be performed through the process. Next, we explore the application of FL in healthcare in order to maintain data privacy while obtaining robust predictive models. FL utilizes aggregated decentralized data from multiple healthcare providers to reduce the chance of data breaches and continue to be compliant with rigorous regulatory frameworks, like HIPAA and GDPR, while doing so. In this work, we evaluate the model performance of FL models in predicting different types of health outcomes in comparison with traditional centralized models. We show that FL can achieve similar predictive accuracy while maintaining data privacy, which makes the choice of FL as a viable alternative to privacy sensitive healthcare environments. Second, applying FL is very challenging: there are communication overhead, data heterogeneity, and security risks. This study also considers ways to minimize these challenges. The results demonstrate the promise of Federated Learning as an innovative paradigm for applying predictive analytics in healthcare, indicative of a robust shift toward more secure, efficient and privacy preserving healthcare systems. © 2025 IEEE.

关键词： Federated learning

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Singer Separation for Karaoke Content Generation 27

Singer Separation for Karaoke Content Generation

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27th Conference on the Oriental COCOSDA International Committee for the Co-Ordination and Standardisation of Speech Databases and Assessment Techniques, O-COCOSDA 2024

作者： Lin, Hsuan-Yu Chen, Xuanjun Jang, Jyh-Shing Roger Graduate Institute of Networking and Multimedia National Taiwan University Taipei Taiwan Graduate Institute of Communication Engineering National Taiwan University Taipei Taiwan National Taiwan University Dept. of Computer Science and Information Engineering Taipei Taiwan

ISBN: (纸本)9798331506032

Due to the rapid development of deep learning, we can now successfully separate singing voice from mono audio music. However, this separation can only extract human voices from other musical instruments, which is undesirable for karaoke content generation applications that only require the separation of lead singers. For this karaoke application, we need to separate the music containing male and female duets into two vocals, or extract a single lead vocal from the music containing vocal harmony. For this reason, we propose in this article to use a singer separation system, which generates karaoke content for one or two separated lead singers. In particular, we introduced three models for the singer separation task and designed an automatic model selection scheme to distinguish how many lead singers are in the song. We also collected a large enough data set, MIR-SingerSeparation11, which has been publicly released to advance the frontier of this research. Our singer separation is most suitable for sentimental ballads and can be directly applied to karaoke content generation. As far as we know, this is the first singer-separation work for real-world karaoke applications. © 2024 IEEE.

关键词： Source separation

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Fake News Prediction in Hindi and English Robust Transformer Model

Fake News Prediction in Hindi and English Robust Transformer...

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2023 International Conference on Innovative Computing, Intelligent communication and Smart Electrical systems, ICSES 2023

作者： Sanmugasundaram, R. Dileepan, D. Manikandan, M. Devi, M. Kiruthiga Ramya, N. Rajalakshmi Institute of Technology Dept. of Electronics and Communication Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Dept. of Electronics and Communication Chennai India Dr. M.G.R. Educational and Research Institute Dept. of Information Technology Chennai India Sri Sairam Engineering College Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350319200

The ever-growing consumption of information from digital platforms such as social media has paved the way for information dissemination like never before, but not necessarily all the information or news delivered to us is authentic or holds true to reality. A huge amount of information is created and shared by social media users, some of which are manipulative and have no influence on reality, and can cause harm in a variety of ways. Implementation of an automated or AI-based system to classify text articles as misinformation is a challenging task as identifying the truth is complicated. An AI-based fake news prediction system is proposed to classify fake news from real ones by understanding the context of news and making decisions accordingly. To identify fake news by examining the context of the news, a Bi-directional Encoder Representations from Transformers (BERT) model, a pretrained model with a more potent feature extractor, has been implemented. BERT is well suited for tasks like predicting fake news because of its extensive contextualizing capabilities. The novelty of this paper is that the proposed system is trained, tested, and evaluated in 2 languages, English and Hindi to develop an overall reliable system to predict fake news in both languages. Two models of BERT namely DeBERTa and XLM-RoBERTa are chosen for the prediction of fake news. Further improvement in the model is achieved by using the additional news data to pre-train this model. Also, the model is fine-tuned to fit the requirements and to get the optimum result possible. The performance comparison of the chosen models is made and it is observed that the XLM-RoBERTa has improved accuracy over DeBERTa while testing for both languages. © 2023 IEEE.

关键词： API interface DeBERTa encoder fake news GPU XLM-RoBERTa

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RadioModRec-l: A Large Scale Radio Frequency Dataset for Automatic Modulation Recognition Research 4

RadioModRec-l: A Large Scale Radio Frequency Dataset for Aut...

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4th International Conference on Emerging Trends in Networks and computer communications, ETNCC 2024

作者： Adetiba, Emmanuel Olasina, Jamiu R. Obiyemi, Obiseye Covenant University Covenant Applied Informatics and Communication Africa Center of Excellence Dept. of Electrical and Information Engineering Ota Nigeria HRA Institute for Systems Science Durban University of Technology Durban4001 South Africa Federal Polytechnic Ilaro Dept. of Computer Engineering Ilaro Nigeria Durban University of Technology Space Science Centre Dept. of Electrical Power Engineering Durban South Africa

ISBN: (纸本)9798350353266

Access to well-curated large datasets remains a significant bottleneck in AI-based research within wireless communication. Rapid advancements in neighbouring fields, such as computer vision and natural language processing, are largely due to the availability of extensive open-access datasets. However, similar progress has not been observed in wireless communication. To address this gap, we curated a comprehensive dataset for fifteen digital modulation schemes, including 4QAM, 16QAM, 64QAM, 256QAM, 8PSK, 16PSK, 32PSK, 64PSK, 128PSK, 256PSK, CPFSK, DBPSK, DQPSK, GFSK, and GMSK. Our dataset considers Rayleigh and Rician channel models under Additive White Gaussian Noise (A WGN) with SNRs ranging from -20dB to + 20dB in 5dB increments. The data samples were converted to constellation signal images and carefully pre-processed. Named RadioModRec, this dataset provides a valuable resource for researchers to train and evaluate AI models. It is freely accessible on Kaggle, promoting further innovation in the wireless communication domain. © 2024 IEEE.

关键词： Signal to noise ratio

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Real-Time Face Mask Detector 3

Real-Time Face Mask Detector

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3rd IEEE International Conference on Technology, engineering, Management for Societal Impact using Marketing, Entrepreneurship and Talent, TEMSMET 2023

作者： Patel, Vishwajeet Guru Prasad, M.S. Aditya Pai, H. Kumar, A Suresh Praveen Gujjar, J. Naveen Kumar, H.N. Dept. of Computer Science and Engg. Dehradun India JAIN [Deemed-to-be University Faculty of Management Studies Bengaluru India Vidyavardhaka College of Engineering Dept. of Electronics and Communication Engg. Karnataka Mysuru India

ISBN: (纸本)9798350397819

When the COVID-19 pandemic was present we all have faced various challenges in our everyday lives. Pandemic created worldwide emergency which led to the lockdown in most of the countries resulting into disruption in world trade and movement. To reduce the covid cases WHO recommended to wear mask and maintain social distancing and then these steps became mandatory for some places, such as restaurants, hospitals, grocery stores etc., The purpose of limiting interaction with individuals as much as possible in such situations is simply defeated by having a staff personally check each person to make sure their mask is on. To avoid this dilemma, it became necessary to build a project named 'Real-time face mask detector', This reveals whether a mask-wearing individual is there. This project needed the use of some python libraries like TensorFlow, Keras, OpenCV. Using this project, we will be able to Detect mask-on faces in live video clip with accuracy of 97-98 %. © 2023 IEEE.

关键词： COVID-19

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Stock Index Probability Prediction using the FB Prophet Model 1

Stock Index Probability Prediction using the FB Prophet Mode...

引用

1st International Conference on Futuristic Technologies, INCOFT 2022

作者： Harish, Neha Likith, H. Yashwanth, Gundala Krishnaswamy, Narayan Eshanya Sunil, G.L. Sai Vidya Institute of Technology Dept. of Computer Science and Engineering Bengaluru India Sai Vidya Institute of Technology Dept. of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9781665450461

Given how unpredictable the stock market has recently become, it is now crucial to be able to accurately predict the future trend of equities. Because of the financial crisis, it is now essential to estimate stock values accurately in order to make money. Non-linear signal forecasting requires sophisticated statistical models and machine learning methods. We must identify the stock's purchasing and selling points for a specific stock market history in order to make a profit. The Dow Theory, MACD, the Relative Strength Index (RSI), the Exponential Moving Average, and others have all been used to forecast stock market performance. It could be challenging to make a specific judgement because these models could provide a variety of indications. The aforementioned issue was attempted to be solved by a wave of machine learning algorithms in artificial intelligence. Some strategies have yet to provide promising results, while others have not reacted as well on the stock market exchange, due to the non-linear structure of the stock market signals. We intend to employ the Prophet model, which is reliable and generates findings with minimal data. The Prophet refers to the method of forecasting time series data based on an additive model with non-linear trends suited with holiday impacts, daily seasonality, weekly, and yearly. Strong seasonal impacts in time-series data are ideal for it. The prophet is resilient to missing data and adept.at handling outliers. Facebook has made available the prophet, an open-source Python package. Predictions of stock prices are shown by indicators like RSI, MACD, Super Trend and others. © 2022 IEEE.

关键词： Machine learning

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Comparative Study of Deep Learning Methods through Improvement of Skin Image Quality of Basal Cell Carcinoma 6

Comparative Study of Deep Learning Methods through Improveme...

引用

6th International Seminar on Research of Information Technology and Intelligent systems, ISRITI 2023

作者： Jannah, Ade Miftahul Muchtar, Kahlil Oktiana, Maulisa Rahman, Aulia Cahyadi, Yagus Maulina, Novi Universitas Syiah Kuala Dept. of Electrical and Computer Engineering Aceh Indonesia Universitas Teknologi Digital Indonesia Dept. of Information System Yogyakarta Indonesia Universitas Syiah Kuala Indonesia Faculty of Medicine Aceh Indonesia

ISBN: (纸本)9798350358346

Basal cell carcinoma (BCC) or commonly known as basal cell cancers is the most common non-melanoma skin disease in the world. BCC is the result of an interaction between a combination of genetic and environmental factors that are directly related to prolonged exposure to ultraviolet (UV) rays, such as the neck and head area. This study proposes to use the CLAHE (Contrast Limited Adaptive Histogram Equalization) method on skin images to improve image quality. CLAHE is an image quality improvement method that aims to obtain images with better contrast than the original image without reducing the quality of the image itself. The experimental results show that the CLAHE image are more accurate than the original image. For quantitative measurements, the performance is seen from the value of accuracy, precision, recall, specificity, and F-score. To be more specific, the experiments utilized the well-known public dataset called ISIC 2018 Dataset (for BCC image), while the normal skin used our own USK-Normal Skin dataset. The results for original image using VGG-16 architecture is 98,3%, ResNet-34 is 97,5%, EfficientNetB0 is 98,2%, and EfficientNet-B7 is 99,7%, respectively. Meanwhile, in the CLAHE image, the accuracy values obtained are higher, for the VGG-16 is 100%, ResNet-34 99.5%, EfficientNetB0 100% and EfficientNet-B7 100%, respectively. The normal skin dataset is available through this link: https://***/USK-NORMAL-SKIN-DATASETI. © 2023 IEEE.

关键词： Image enhancement

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Web Scraping Approaches and their Performance on Modern Websites 3

Web Scraping Approaches and their Performance on Modern Webs...

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3rd International Conference on Electronics and Sustainable communication systems, ICESC 2022

作者： Bale, Ajay Sudhir Ghorpade, Naveen Rohith, S. Kamalesh, S. Rohith, R. Rohan, B.S. New Horizon College of Engineering Dept. of Electronics and Communication Engineering Bengaluru India Vijay Vittal Institute of Technology Dept. of Computer Science Engineering Bengaluru India Reva University School of Computer Science and Engineering Bengaluru India

ISBN: (数字)9781665479714

ISBN: (纸本)9781665479714

When it comes to information, the internet is a gold mine. Whether you need data for your business, school, or personal use, you may uncover a wealth of information by performing an internet search. Web Scraping (WS) is a computerized method of obtaining big amounts of information from internet sites. The bulk of this information is in the form of unstructured HTML that would be transformed to structured data in a digital document before use in diverse applications. Although web scraping is not illegal, it might go against the ethics. This paper contains research-based findings of different methods of web scraping techniques used to extract data from websites. The various approaches used in this paper to obtain the results include requests library, selenium and other external libraries. Using the results obtained the findings are visualized through different graphs having contrasting parameters as measurements. Results obtained from our paper show that there is a lot of improvement needed to protect websites from web scraping bots. © 2022 IEEE.

关键词： Websites

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Rethinking on Ciphertext Equality Check of Decapsulation of NIST PQC Standardization 3rd Round Finalist Candidate Saber 13

Rethinking on Ciphertext Equality Check of Decapsulation of ...

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13th International Conference on Information and communication Technology Convergence, ICTC 2022

作者： Lee, Dong-Hoon Seo, Eun-Young Kim, Young-Sik No, Jong-Seon Seoul National University Dept. Electrical and Computer Engineering Seoul Korea Republic of Chosun University Dept. Information and Communication Engineering Gwang-ju Korea Republic of

ISBN: (数字)9781665499392

ISBN: (纸本)9781665499392

In this paper, we rethink the ciphertext equality check step of the decapsulation procedure of Saber, which is one out of four KEM finalists in the third round NIST PQC standardization process. Through this investigation, we pursue cryptographic answers to some important questions. Those are why the re-encryption process of Fujisaki-Okamoto transformation, which is commonly used method to satisfy IND-CCA security in most existing lattice-based KEM algorithms including Saber, is especially necessary in the case of Saber, whether the re-encryption process can be further simplified, and whether the ciphertext equality check must be performed over the entire (re-encrypted/originally received) ciphertexts? © 2022 IEEE.

关键词： Standardization

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Performance Evaluation of Synthetic Aperture Radar Images 8

Performance Evaluation of Synthetic Aperture Radar Images

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8th International Conference on Signal Processing and communication, ICSC 2022

作者： Upadhyay, Kritika Kumar, Sandeep Bharti, Manisha Agarwal, Prabhakar National Institute of Technology Delhi Dept. of Electronics and Communication Engineering Delhi India National Institute of Technology Delhi Dept. of Computer Science and Engineering Delhi India

ISBN: (纸本)9781665454308

Synthetic Aperture Radar (SAR) imaginary is used extensively for Military applications in the modern era of technology. In this work, we have evaluated the performance of SAR images for Radar systems. The SAR length, Resolution, and Footprint of SAR networks have been compared by varying the parameters of Radar signals like Bandwidth, Operating Frequency, and Satellite Altitude. From the results, it was observed that Antenna Footprints vary inversely with the Operating Frequency and Grazing Angle of Radar © 2022 IEEE.

关键词： Synthetic aperture radar

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