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Federated Learning for Residential Load and PV Production Forecast 13

Federated Learning for Residential Load and PV Production Fo...

引用

13th IEEE Power and Energy Society: Innovative Smart Grid Technologies - Asia, ISGT Asia 2024

作者： Naladkar, Gargi Yadav, Amit Gugaliya, Jinendra Indian Institute of Technology Kanpur Computer Science & Engineering Department Kanpur India Enphase Solar Energy Pvt.Ltd Sr. Engineer Data Science Cto India Bangalore India Enphase Solar Energy Pvt.Ltd AI/ML Scientist - Cto Office Bangalore India

ISBN: (纸本)9798350373929

This paper explores the application of federated learning in predicting energy consumption and solar production for households equipped with Enphase smart home solar systems. Traditional centralized model training for energy prediction entails significant communication overhead, particularly when dealing with distributed data sources. To mitigate this challenge, federated learning is proposed as a privacy-preserving alternative, wherein each client trains a local model on its data and transmits model updates to a central server for aggregation. We propose a neural network-based energy demand predictor trained with federated learning, leveraging distributed data while maintaining prediction accuracies. Federated learning ensures data security and confidentiality by transmitting only model updates, preserving individual privacy. The datasets used are anonymized, and the actual data cannot be extracted from the shared gradients. Through experiments, we demonstrate the feasibility and effectiveness of federated learning in energy forecasting, highlighting its potential to enhance prediction capabilities without compromising privacy. © 2024 IEEE.

关键词： Federated learning

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Enhancing Data Privacy and Accountability in Smart Grids with Blockchain Technology

Enhancing Data Privacy and Accountability in Smart Grids wit...

引用

Electrical Electronics and Computing Technologies (ICEECT), International Conference on

作者： G. Indiravathi M. P. Kumar Anumulagundam Usha Roshan Kumar T. Lohith Shaik Shahid Department of Computer Science and Engineering Siddharth Institute of Engineering & Technology Chittoor Andhra Pradesh India Department of Computer Science and Engineering (AI&ML) Siddharth Institute of Engineering & Technology Chittoor Andhra Pradesh India

ISBN: (数字)9798350378092

ISBN: (纸本)9798350378108

Smart grids offer promising opportunities for energy efficiency but raise concerns about data privacy and transparency. This paper presents a new framework for Secure and Auditable Private Data Sharing designed specifically for smart grids, with a focus on its functionality as a password manager. By utilizing smart contracts and blockchain technology, the framework creates explicit guidelines for data usage that guarantee clear oversight of data access and intended uses. In addition to managing smart grid data, the framework securely stores passwords and credentials, providing users with a comprehensive solution for data security and access management. The use of off-chain smart contract execution and trusted execution environments maximizes performance. To further assure transactional reliability during the result dissemination and payment procedures, the framework employs a two-phase atomic delivery mechanism. Energy service providers gain from optimum contracts because they encourage high-quality data exchange and customer participation through the use of contract theory. Extensive simulations validate the efficacy of the proposed approach, enabling users to securely access their data remotely through a cloud platform integrated with open-source services.

关键词： Data privacy Data security Smart contracts Passwords Reliability theory Energy efficiency Data models Smart grids Blockchains Guidelines

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Brain Tumor Detection Using ADARN Optimizer 2

Brain Tumor Detection Using ADARN Optimizer

引用

2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Kumar, Rajiv Dwivedi, Avinash Joshi, Shivani Tripathi, Vikas Chandra Nvidia Ai Learning & Research Center G L Bajaj Institute of Technology and Management Department of Applied Computational Science and Engineering Greater Noida India Galgotias University Department of Computer Science & Engineering Greater Noida India G L Bajaj Institute of Technology and Management Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350371055

The use of MRI scans to identify and categorise brain tumors is the main focus of this study. The goal is to develop a precise and reliable method for early identification and accurate categorisation of brain tumours, including pituitary neoplasms, meningiomas, and gliomas. Deep learning methods were utilized to analyze MRI image databases, resulting in a 96% identification accuracy for brain tumors and a 98% categorization accuracy for the three types. This research demonstrates the potential of deep learning techniques for accurate brain tumor identification and categorization. The early identification and precise categorization of brain tumors can assist medical professionals in making informed decisions about the best treatment options for patients, leading to better outcomes and survival rates. The article provides an extensive review of current methods for identifying and classifying brain tumors using MRI data. It emphasizes the critical importance of this field of study in improving patient outcomes and reducing unnecessary treatments. © 2024 IEEE.

关键词： Brain Tumor Classification Convolutional Neural Network identification Image Augmentation MRI Scans

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Automating ml Models Using mlOPS 2

Automating ML Models Using MLOPS

引用

2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Sirisha, N. Kiran, Ajmeera Arshad, Mohammad Mounika, M. Mlr Institute of Technology Department of Computer Science and Engineering Hyderabad Dundigal India Deccan College of Engineering and Technology Department of It India B v Raju Institute of Technology Department of Ai & Ds India

ISBN: (纸本)9798350370188

Machine learning engineering relies on mlOps, a collection of best practises for commercializing models. As fresh data is added, machine learning models can become less effective. ml models can't use all data. To do this, we must reimagine the model by changing the front and back ends. Our project regularly tracks and trains models to overcome this constraint. mlOps (Machine Learning Operations) can help. ml pipeline automation integrates the trained model into the cloud, which can take a dataset as input and output. To quickly update the cloud ml algorithm in response to fresh data, we don't need to change the model. This guarantees the chosen model is valid and can be used with fewer flaws, improving performance. Our main objective is to build a reliable automated pipeline and install, maintain, and optimise machine learning models and systems securely and efficiently. © 2024 IEEE.

关键词： Machine learning

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Securing Wireless and Optical Networks: Advanced Strategies for Network and Information Security in Modern Communication Systems

Securing Wireless and Optical Networks: Advanced Strategies ...

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

作者： Dhasmana, Gulshan Sharma, Vishal Boob, Nandini Shirish Aravind, K. Reddy, R. Akhilesh Yadav, Kanchan Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Lovely Professional University Phagwara India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India New Horizon College of Engineering Department of Electronics and Communication Engineering Bangalore India MLR Institute of Technology Department of CSE-AI&ML Telangana Hyderabad India GLA University Department of Electrical Engineering Mathura India

ISBN: (纸本)9798331512965

In the era of rapidly changing communication systems, securing wireless and optical networks has become of essence to protect sensitive data and ensure operation. This study addresses network and information security in current communication systems by studying cutting edge techniques to enhance network and information security in wireless and optical networks. The wireless networks are convenient and may be moved around, but they are also prone to hacking, illegal access or some other cyberattacks. As optical networks possess both a large capacity and relatively low latency, it is not without special security issues such as fibre tapping and signal jamming. This study presents detailed analyses of state of the art authentication techniques, intrusion detection systems (IDS), and encryption algorithms specifically designed for these networks. Then there are, such as Quantum Key Distribution (QKD) which almost assuarly unbreakable techniques are examined alongside conventional cryptographic methods. Additionally, anomaly detection based on machine learning is explored for real time threat identification in optical and wireless channels. It stresses the need of the cross domain solutions and multi layered security frameworks covering the network layer and the physical security measures. Through case studies and recent developments in cyber threats and network resilience, this article provides a thorough understanding of how these tactics could be used to make networks such as wireless network and optical network more resilient against increasing cyber risks. © 2025 IEEE.

关键词： Network intrusion

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OHDLL:Optimized Hybrid Deep Learning Model for Classification of Leukaemia images

OHDLL:Optimized Hybrid Deep Learning Model for Classificatio...

引用

2023 International Conference on the Confluence of Advancements in Robotics, Vision and Interdisciplinary Technology Management, IC-RVITM 2023

作者： Saju, Binju Asha, V. Tressa, Neethu Suhas, R.A. Jagesh, Shah Yash Sandhya, V. Adi Shankara Institute of Engineering & Technology Department of Computer Science - Ai Engineering Kerala Kalady India Master of Computer Applications New Horizon College of Engineering Bengaluru India

ISBN: (纸本)9798350306088

A serious haematological cancer that causes mortality and morbidity in people of all ages is leukaemia. The haematologists examine bone marrow and blood to determine Leukaemia. Techniques for manual blood testing that have been in use for a long period are frequently slow and produce less reliable diagnoses. The study proposes a hybrid model to classify leukaemia cell images. Initially, images are pre-processed which includes Image resizing, Gray scale conversion and noise removal using a combination of Weiner filter and Median filter. Further, images are enhanced using Gaussian Mixture based histogram equalization. Features of the images are extracted using Dense Convolutional neural network. An optimized ResNet101 model with improved Aquila optimization is used for classification. Performance of the models is compared with other studies in literature. The proposed model has obtained an F1 score of 98.98%, an accuracy of 98.20%, sensitivity of 98.9%, specificity of 98.1%, and precision of 98%. © 2023 IEEE.

关键词： Median filters

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Diffusion Inference with Dynamic Classifier-free Guidance

Diffusion Inference with Dynamic Classifier-free Guidance

引用

International Conference on Inventive Computing and Informatics (ICICI)

作者： Aryan V S Ashish Kulkarni Dhruv Chawla Aryan Rawther Jayashree Rangareddy Department of Computer Science PES University Bengaluru Karnataka India Department of Computer Science (AI & ML) PES University Bengaluru Karnataka India

ISBN: (数字)9798350373295

ISBN: (纸本)9798350373301

The introduction of the diffusion architecture has led to rapid progress in image generation. These models learn to iteratively generate images by predicting noise from an initial Gaussian distribution, which is learnt through forward and reverse diffusion. The process of iterative sampling occurs over a number of inference steps. Classifier guidance is a popular technique that guides the generation process of latent diffusion models (LDMs). It does this by adding an external classifier into the training process. This however requires additional training and severely limits the diversity of the synthesized data along with several other limitations. To improve on this, a novel approach, known as Classifier-Free Guidance (CFG), was introduced that removed the need for a separate classifier. It controls the sample generation process using a parameter known as the CFG scale. Previously, this scale has been set to a constant value during inference. This research work proposes varying the CFG scale values across the inference steps by making use of various scheduling functions, which not only results in better images but also unlocks the full potential of the rich latent space representation of diffusion models by allowing for the sampling of various different images from the same initial conditions by only varying the CFG scale and keeping all other parameters constant.

关键词： Training Measurement Schedules Noise Process control Aerospace electronics Predictive models

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A Real-time Sign Language Recognition System using MediaPipe and Random Forest Classifier combining with Text-to-Speech Technique

A Real-time Sign Language Recognition System using MediaPipe...

引用

Artificial Intelligence and Data engineering (aiDE), International Conference on

作者： Sanapareddi Gari Yuva Nithesh Reddy Subhash Mondal P M G Bashir Asdaque K Vamsi Krishna Computer Science & Engineering (AI & ML) Dayananda Sagar University Bengaluru India Mechanical Engineering Dayananda Sagar University Bengaluru India

ISBN: (数字)9798331527518

ISBN: (纸本)9798331527525

This research study introduces a real-time sign language detection system utilizing hand gestures, specifically developed to bridge the communication gap for deaf and hard-of-hearing individuals. The system employs advanced computer vision techniques, utilizing MediaPipe for accurate hand tracking and landmark detection, extracting key features such as fingertip and palm positions. These features are normalized to address variations in hand size and orientation, ensuring consistent input for classification. A Random Forest Classifier (RFC), trained on a labeled dataset of gestures, processes the data to achieve reliable recognition. The system offers dual feedback mechanisms: real-time visual text overlays on live video streams and Text-to-Speech (TTS) output, making it intuitive and user-friendly. Iterative training and out-of-bag error monitoring enhance model performance, ensuring seamless functionality across devices ranging from mobile phones to larger systems. Motivated by the need for inclusivity, this solution enables effortless communication in both social and professional contexts. By leveraging ai-driven approaches, the system provides an accessible and efficient tool for promoting understanding and interaction, advancing assistive technologies for deaf and hard-of-hearing communities. This work represents a significant step toward creating more inclusive communication solutions.

关键词： Hands Training Sign language Visualization Technological innovation Accuracy Feature extraction Real-time systems Text to speech Random forests

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Machine Learning Challenges of E-government Models of Cloud Computing in Developing Countries 18

Machine Learning Challenges of E-government Models of Cloud ...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Tamilarasi, R. Kumar, P. N. Santhosh Ghantasala, G. S. Pradeep Rao, D. Nageswara Bathla, Priyanka Gupta, Gaurav Sonia Alliance University Department of Computer Science and Engineering Bengaluru India Hyderabad India Alliance University Department of Computer Science and Engineering Bangalore India Chandigarh University Punjab India Shoolini University Solan India Shoolini University Yogananda School of AI Computer and Data Sciences Solan India

ISBN: (纸本)9789380544519

Cloud computing usage in electronic government (e-government) is developing across nations and presents a range of difficulties. Infrastructure problems, such as unreliable internet connections and insufficient devices, make it difficult to integrate cloud solutions efficiently. Concerns about data security and privacy arise when private information about citizens is stored in the cloud and could be hacked or accessed without permission. Economic limits and resource constraints could prevent cloud infrastructure installation, thereby reducing e-government service scalability and efficiency. Compliance with various regulations adds complexity to the legal and governmental aspects of managing cloud data. A scientific investigation reveals that while cloud computing-driven e-government services offer numerous advantages, factors such as IT infrastructure, internet accessibility, and confidence in emerging technologies could pose barriers to their adoption. Also, recommend trust-focused cloud computing solutions for e-government services (CCSE-GS). Finally, this study focuses on the number of obstacles that developing countries must overcome to fully realize the potential of cloud computing for e-government services. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Challenges about e-government Cloud Computing E-Government models IT infrastructure

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Advances in Big Data and Data Mining: Techniques and Applications in Data Fusion for Enhanced Insights and Decision-Making

Advances in Big Data and Data Mining: Techniques and Applica...

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

作者： Goyal, Himanshu Rai Shrivastava, Anurag Nagpal, Amandeep Reddy, R. Akhilesh Yadav, Kanchan Revathi, R. Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India Lovely Professional University Phagwara India MLR Institute of Technology Department of CSE-AI&ML Telangana Hyderabad India GLA University Department of Electrical Engineering Mathura India New Horizon College of Engineering Department of Applied Sciences Bangalore India

ISBN: (纸本)9798331512965

The advancement of big data has accelerated over the years to change how companies utilize data to drive decisions. And so, this paper aims to look at big data and data mining in different perspectives while highlighting the importance of data fusion in decision making in the current society. Many times, data fusion combines different data sources to provide better and broader results. Clustering of raw data, anomaly detection and machine learning are discussed as well as their use in healthcare, financial management and smart cities. This paper also presents such issues as data heterogeneity, data quality assurance, as well as scalability issues. Lastly, tomorrow's possibilities, such as artificial intelligence and quantum computing, are presented as possible enhancements of the data fusion effectiveness are mentioned due to the expected marked advancement in real-time and adaptive decisionmaking systems. © 2025 IEEE.

关键词： Data centers

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