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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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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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A Transfer Learning Based Web App for Glaucoma Detection Using Low-Cost Ophthalmoscopic Camera

A Transfer Learning Based Web App for Glaucoma Detection Usi...

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Network, Multimedia and Information Technology (NMITCON) International Conference on

作者： B K Chethan Sanjana Vishwanath Sambargi D Roopashree K Prabhavathi Kiran Puttegowda D S Sunil Kumar Department of Computer Science Engineering (AI&ML) Vidyavardhaka College of Engineering Mysuru India Department of AI and ML Coorg Institute Of Technology Ponnampet Karnataka India Department of ECE Government Engineering College Hassan Karnataka India Department of ECE BGS Institute of Technology Adichunchanagiri University Mandya Karnataka India Department of ECE Vidyavardhaka College of Engineering Mysuru Karnataka India Anatta innovations BGS B-School Bengaluru India

ISBN: (数字)9798350372892

ISBN: (纸本)9798350372908

Glaucoma has been the most fundamental cause of irreversible blindness in persons over a few decades. This condition is extremely prevalent in several nations, and in the worst-case situation is that many countries do not have enough ophthalmologists. According to data from global glaucoma research, roughly 12 million individuals would be symbiotically blind from early-stage glaucoma by 2020. To evaluate the disease in the eye, fundus photography is used. Fundus photos require a lengthy visual inspection. The transfer-learning based web-app for glaucoma detection utilizing low-cost ophthalmoscopic cameras is presented in this paper. Fundus evaluation is an important part of an eye exam that helps the web-app provide vital diagnostic information to both ophthalmologists and non-ophthalmologists. Vision impairment occurs over time because it is not a rapid consequence. Only in the later stages those signs are noticed. The optic nerve was studied using 2-Dimensional retinal fundus pictures, which is a difficult task that can result in a significant cost for detecting regular glaucoma. There are a variety of different tests that can be used to rule out glaucoma, including tonometry which signifies pressure on eye, gonioscopy which examines angle is closed or open, OCT - Optical Coherence Tomography, Ophthalmoscopic Imaging to visualize retina as well as optic nerve and Fundus Copying. In this paper, an effective embedded based eye testing architecture is presented.

关键词： Glaucoma Performance evaluation Biomedical optical imaging Transfer learning Medical services Cameras Retina Optical imaging Adaptive optics Servers

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

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

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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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Energy Harvesting in Cognitive Radio Networks using Local Search based Dingo Optimization Algorithm

Energy Harvesting in Cognitive Radio Networks using Local Se...

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Distributed Systems, computer Networks and Cybersecurity (ICDSCNC), International Conference on

作者： Sripelli Jagadish Ramy Riad Al-Fatlawy Ankam Sreejyothsna Syed Salim Sowmya M School of Computer Science & Artificial Intelligence SR University Warangal India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of AI&ML Gokaraju Rangaraju Institute of Engineering and Technology Bachupally India Department of Computer Science and Design School of Engineering Mysore University Manasa Gangothiri Mysuru India Department of AI & DS Nitte Meenakshi Institute of Technology Bengaluru India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

Cognitive Radio Networks (CRNs) have been introduced as an empowering advancement which enables the dynamically unutilized spectrum to enhance the spectral effectiveness. Energy efficiency and the throughput are the apprehensions for the energy-harvesting CRN. Nevertheless, obtaining a greatest maximum level of these metrices needs the optimization of sensing duration, transmission time and the energy harvesting. To tackle these problems, this articles purposes to propose the Local Search based Dingo Optimization Algorithm (LS-DOA) approach for energy harvesting in CRN. The LS-DOA enhances an energy harvesting procedure through balancing an energy collected and obtained. This flexibility results in effective energy management as well as make sures that energy is significantly harvested from the obtainable ambient playforms. LS-DOA is robust in dynamic environments, regulating to changeable spectrum obtainability and different energy harvesting environments. This designs it optimal for CRNs, where network conditions are highly variable. The multi-objective functions such as energy efficiency and throughput are considered to estimate the model effectiveness. The experimental results shows that the proposed LS-DOA approach attains the average throughput and energy efficiency of 0.241 and 2.938 as compared to the existing approaches like DOA and Pelican Optimization Algorithm (POA).

关键词： Wireless networks Heuristic algorithms Metaheuristics Throughput Search problems Energy efficiency Real-time systems Sensors Energy harvesting Cognitive radio

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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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Smart Home Automation Using ai and IoT with High Security 2

Smart Home Automation Using AI and IoT with High Security

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

作者： Rani, K. Pushpa Sreedevi, Pogula Veeranjaneyulu, P. Kanth, M. Ravi Allam, Sangeetha Mohanty, Jnyana Ranjan Mlr Institute of Technology Department of Computer Science Engineering Hyderabad500043 India G.Pulla Reddy Engineering College Department of Computer Science and Engineering Kurnool518007 India and Ai&ds Hyderabad India Vnr Vignana Jyothi Institute of Engineering and Technology Department of Cse Hyderabad India Mlr Institute of Technology Department of Computer Science & Engineering Hyderabad India Kalinga Institute of Industrial Technology University India

ISBN: (纸本)9798350370188

A well-known and widely utilized technology is smart home automation. The project's primary goal is to create an android-based, Wi-Fi enabled home automation which makes our daily life simpler and easier. in every automation technology which uses internet of things which is a developing network of common items that can share information and carry out tasks while you are occupied with other activities, from industrial machinery to consumer goods. A system known as a Wireless Home Automation System (WHAS) integrating ai & IOT allows users to remotely control common home features and operations using PCs or mobile devices. Both human and electric energy are to be conserved. Because it permits, there is a difference between other systems and home automation systems. © 2024 IEEE.

关键词： Automation

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Neural Networks Meet Neural Activity: Utilizing EEG for Mental Workload Estimation 27th

Neural Networks Meet Neural Activity: Utilizing EEG for Ment...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Siddhad, Gourav Roy, Partha Pratim Kim, Byung-Gyu Department of Computer Science and Engineering Indian Institute of Technology Roorkee Uttarakhand Roorkee247667 India Division of AI Engineering Sookmyung Women’s University Seoul04310 Korea Republic of

ISBN: (纸本)9783031781940

Electroencephalography (EEG) offers non-invasive, real-time mental workload assessment, which is crucial in high-stakes domains like aviation and medicine and for advancing brain-computer interface (BCI) technologies. This study introduces a customized ConvNeXt architecture, a powerful convolutional neural network, specifically adapted for EEG analysis. ConvNeXt addresses traditional EEG challenges like high dimensionality, noise, and variability, enhancing the precision of mental workload classification. Using the STEW dataset, the proposed ConvNeXt model is evaluated alongside SVM, EEGNet, and TSception on binary (No vs SIMKAP task) and ternary (SIMKAP multitask) class mental workload tasks. Results demonstrated that ConvNeXt significantly outperformed the other models, achieving accuracies of 95.76% for binary and 95.11% for multi-class classification. This demonstrates ConvNeXt’s resilience and efficiency for EEG data analysis, establishing new standards for mental workload evaluation. These findings represent a considerable advancement in EEG-based mental workload estimation, laying the foundation for future improvements in cognitive state measurements. This has broad implications for safety, efficiency, and user experience across various scenarios. Integrating powerful neural networks such as ConvNeXt is a critical step forward in non-invasive cognitive monitoring. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Electroencephalography

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Human Activity Recognition Using Improved Cat Swarm Optimization Algorithm and Convolutional Neural Network 2

Human Activity Recognition Using Improved Cat Swarm Optimiza...

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2nd IEEE International Conference on Networks, Multimedia and Information Technology, NMITCON 2024

作者： Chanti, Yerrolla Shnain, Ammar Hameed Banoth, Ramesh Rupavath, Rana Veer Samara Sihman Brahmatej Sushama, C. Sr University School of Computer Science & Artificial Intelligence Warangal India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Gokaraju Rangaraju Institute of Engineering and Technology Department of Ai & Ml Bachupally Hyderabad India Indiana Wesleyan University Marion United States Mohan Babu University School of Computing Department of Cse Tirupati India

ISBN: (纸本)9798350372892

Widespread computing has a lot of ability to offer users customised services and support, especially as more people use smartphones for everyday tasks. In the past ten years, there has been a significant advancement in Sensor-Based Human Activity Recognition (HAR), which detects the movements of the user and emerged as a crucial component of pervasive computing. Thus, accurate HAR plays a significant role in numerous applications. For accurate HAR, the current research proposes an Improved Cat Swarm Optimization Algorithm based Convolutional Neural Network (ICSO-CNN) for tuning the parameters of CNN for optimal solution. Using various performance measures, the present research examines the performance of the proposed ICSO-CNN algorithm. The test results demonstrated that the ICSO-CNN outperforms the compared methods, including CNN with Long Short-Term Memory (CNN-LSTM), Particle Swarm Optimization based CNN (PSO-CNN), and CNN-BiLSTM with an accuracy of 99.79%. © 2024 IEEE.

关键词： Convolutional neural networks

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