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International Conference on Futuristic Technologies (INCOFT)

作者： Harsh Mali Amit Nara Jaya Gupta Smit Panchal Department of Computer Engineering A.P.Shah Institute of Technology Thane India Department of Computer Science & Engineering (AI&ML) A.P.Shah Institute of Technology Thane India

Recommendation systems have received a lot of attention lately due to their pivotal role in guiding users through the overwhelming volumes of information available online. This paper explores the integration of Natural Language Processing (NLP) techniques into recommendation systems to enhance their performance and user experience. The growth of the users has been increasing daily, to give them the satisfaction they want when scrolling through the feed. We have made a recommendation system using prominent technology. The system connects the user faster with their desired videos. To make a better system we have used React, Firebase, and Flask as the crucial elements of our work. We recommend the top 5 reels using some aspects. The aspects are hashtags, descriptions, and user id.

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Enhancing Supply Chain Regulatory Compliance with Blockchain and Lasso Regression Integration 2

Enhancing Supply Chain Regulatory Compliance with Blockchain...

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2nd IEEE International Conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Aelgani, Vivekanand Jothi, E. Hussein, Ramy Riad Ramesh, Banoth Sharma, Himanshu Uganya, G. CMR College of Engineering & Technology Department of CSE Telangana Hyderabad India New Prince Shri Bhavani College of Engineering and Technology Department of IT Tamil Nadu Chennai600073 India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq GRIET Bachupally Department of AI&ML Telangana Hyderabad India GLA University Department of Computer Engineering & Applications Mathura India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of ECE Chennai600062 India

ISBN: (纸本)9798350368109

Adopting lasso regression besides blockchain gives the best solution in improving the regulatory compliance in supply chains. For this integration, the feature of blockchain technology which applies for secure, transparency and unchangeable record keeping is used, For lasso regression the variable selection and predictive model are used. Thus, blockchain offers supply chain participants an opportunity to perform all transactions in a decentralized manner and maintain an unalterable, open record of all the transactions that occur in the supply chain. At the same time, the lasso regression models are used to predict compliance risks based on the data obtained and choosing the features that might be useful for this analysis from a vast set of possibilities. Smart contracts are the other strategies used in performing compliance checks in real-time and reporting instances that indicate non-compliance so that the necessary intervention is done. This system does not only administers and optimizes the press compliance procedure but also improves prediction rate and reduces the risk of encountering new compliance issues. Further, the changes are made continuously in the model and the feedbacks from the stakeholders add to the fact that the system is fit to adapt to the changes in the regulatory environment and supply chain industry. In doing so, it also enhances the visibility, relevancy, reliability, and effectiveness of the regulation compliance enhancing better trust and operation in the overall supply chain. © 2024 IEEE.

关键词： Smart contract

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Nonlinear Dynamic Inversion-based Motion Planning of a Floating Satellite Platform

Nonlinear Dynamic Inversion-based Motion Planning of a Float...

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aiAA science and Technology Forum and Exposition, aiAA SciTech Forum 2025

作者： Banerjee, Avijit Tevetzidis, Ilias Satpute, Sumeet Gajanan Nikolakopoulos, George Robotics and AI research group Department of Computer Science Electrical and Space Engineering Luleå University of Technology Luleå Sweden

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

This article presents a nonlinear dynamic inversion-based motion plan for a levitating robotic satellite emulation platform. The frictionless motion of such a levitating platform has dynamic equivalency with a satellite’s realistic movement in space, thus evolving as an efficient demonstration tool as a hardware-in-loop satellite motion simulator. A brief design description of the emulation platform unified with various sensor and actuator units is presented, and an associated nonlinear mathematical model representing its coupled dynamic motion is described. The proposed control design architecture manipulates the associated nonlinear dynamics, which appear due to the coupling of translational and rotational movement of the robotic platform, thereby establishing an efficient trajectory tracking capability for various operating regions without the additional requirement of gain scheduling. In order to realize the control requirement through a set of ON-OFF thruster-based actuation units, which closely resembles the reaction control system of realistic satellite design, an optimal thrust allocation logic is proposed that accounts for various physical limitations of thrusters such as thrusters as its magnitude constraint and minimum on time. The efficacy of the proposed modelling and control scheme is experimentally validated in different realistic motion planning scenarios, such as point-to-point manoeuvres with terminal objectives and a superior path-following capability. The experimental studies are supported with related results. © 2025, American Institute of Aeronautics and Astronautics Inc, aiAA. All rights reserved.

关键词： Motion planning

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Advanced Hybrid Methodology for Robust Heart Disease Prediction and Feature Optimization

Advanced Hybrid Methodology for Robust Heart Disease Predict...

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Machine Learning and Autonomous Systems (ICmlAS), International Conference on

作者： Alekhya G Sudheer Kumar C Ouku Bhulakshmi Harikrishna T V T Ram Pavan Kumar M Boddu L V Siva Rama Krishna Department of CSE (AI&ML) G Pulla Reddy Engineering College Kurnool India Department of CSE Srinivasa Ramanujan Institute of Technology Ananthapuramu India Department of CSE Santhiram Engineering College Nandyal India Department of AI&ML Annamacharya Institute of Technology and Sciences Rajampet India Department of Computer Science Kakaraparti Bhavanarayana College Vijayawada India Department of CSE SRM University Mangalagiri AP India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

This research presents a novel hybrid framework for heart disease prediction, integrating advanced data preprocessing with ensemble learning to enhance diagnostic accuracy. The methodology begins with rigorous data cleansing to ensure reliability, followed by Synthetic Minority Over-sampling Technique (SMOTE) to balance class distribution. Outliers are identified and mitigated using the Z-score method, preserving data integrity. A unique Recursive Hybrid Feature Extraction (RHFE) strategy, combining filter and wrapper techniques, optimizes feature selection by reducing multicollinearity and enhancing model efficiency. Key predictive markers include age, chest pain type, maximum heart rate, ST depression induced by exercise, and major vessel count via fluoroscopy. The refined dataset is used to train an CatBoost -based ensemble model, achieving remarkable performance with 94.2% accuracy, 93.5% precision, 94% recall, and an outstanding ROC AUC score of 0.98. These results highlight the model's robustness and its potential for real-world clinical implementation in early heart disease detection and risk assessment.

关键词： Heart Accuracy Pain Data preprocessing Predictive models Feature extraction Robustness Risk management Ensemble learning Diseases

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Implementing Machine Learning-based Autonomic Cyber Defense for IoT-enabled Healthcare Devices

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Journal of Artificial Intelligence and Technology 2023年第4期3卷 162-172页

作者： M.Manimaran Murali Dhar Roger Norabuena-Figueroa R.Mahaveerakannan S.Saraswathi K.Selvakumarasamy Department of Computer Science and Engineering(AI&ML) School of ComputingMohan Babu UniversityTirupatiAndhra PradeshIndia Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology AvadiChennaiTNIndia Universidad Privada San Juan Bautista LimaPerú Saveetha School of Engineering Saveetha Institute of Medical and Technical SciencesChennaiTNIndia

Smart homes present a serious challenge for the aged and those with mobility issues due to the environment’s inherent *** people have the propensity to fall over when bending over in these ***,they show two time-based reasoning models to identify incidents of potentially fatal falls that have not been accounted for(CM-I and CM-II).The ubiquitous use of IoT altimeter watches among the elderly provides a wealth of data that can be used by these algorithms to predict the likelihood of a fall based on categorization *** compared actual and simulated data involving missteps,mishaps,and crashes to gauge the programmers’*** suggest that using such logic models to help healthcare providers determine if senior people living in smart homes have fallen is a potential field for future *** CM-II model had the highest prediction accuracy of any model identified in the literature,at 0.98 when compared to the test *** the number of devices linked to the IoT can be quickly extended in contrast to the number of devices connected to conventional computers,the number of hacks aimed at the IoT has grown *** is no way to fix the issue that hacked IoT devices create until they figure out how to track down the source of the *** a deeper understanding of the technologies,protocols,and architecture of IoT systems,as well as the potential consequences of using infected IoT devices,is the overarching goal of this *** are many Internet of Things(IoT)systems vulnerable to cybercriminal manipulation,so this study also explores a range of machine learning and deep learning-based methods that can be used to detect such compromise.

关键词： cyber defense healthcare devices Internet of Things(IoT) machine learning

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Consistent Diffusion Meets Tweedie: Training Exact Ambient Diffusion Models with Noisy Data 41

Consistent Diffusion Meets Tweedie: Training Exact Ambient D...

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41st International Conference on Machine Learning, ICml 2024

作者： Daras, Giannis Dimakis, Alexandros G. Daskalakis, Constantinos Department of Computer Science University of Texas Austin United States Archimedes AI United States Department of Electrical and Computer Engineering University of Texas Austin United States Department of Electrical Engineering and Computer Science MIT United States

Ambient diffusion is a recently proposed framework for training diffusion models using corrupted data. Both Ambient Diffusion and alternative SURE-based approaches for learning diffusion models from corrupted data resort to approximations which deteriorate performance. We present the first framework for training diffusion models that provably sample from the uncorrupted distribution given only noisy training data, solving an open problem in Ambient diffusion. Our key technical contribution is a method that uses a double application of Tweedie's formula and a consistency loss function that allows us to extend sampling at noise levels below the observed data noise. We also provide further evidence that diffusion models memorize from their training sets by identifying extremely corrupted images that are almost perfectly reconstructed, raising copyright and privacy concerns. Our method for training using corrupted samples can be used to mitigate this problem. We demonstrate this by fine-tuning Stable Diffusion XL to generate samples from a distribution using only noisy samples. Our framework reduces the amount of memorization of the fine-tuning dataset, while maintaining competitive performance. Copyright 2024 by the author(s)

关键词： Problem solving

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A Neural Computing-Based Access Control Protocol for ai-Driven Intelligent Flying Vehicles in Industry 5.0-Assisted Consumer Electronics

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IEEE Transactions on Consumer Electronics 2024年第1期70卷 3573-3581页

作者： Mahmood, Khalid Tariq, Tayyaba Sangaiah, Arun Kumar Ghaffar, Zahid Saleem, Muhammad Asad Shamshad, Salman National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan National Yunlin University of Science and Technology Department of Computer Science and Information Engineering Douliu64002 Taiwan National Yunlin University of Science and Technology International Graduate School of AI Douliu64002 Taiwan Lebanese American University Department of Electrical and Computer Engineering Byblos1102 2801 Lebanon University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu611731 China The University of Lahore Department of Software Engineering Lahore54590 Pakistan

The continuous revolution in Artificial Intelligence (ai) has played a significant role in the development of key consumer applications, including Industry 5.0, autonomous decision-making, fault diagnosis, etc. In practice, autonomous and intelligent robots and flying vehicles (i.e., drones) are also becoming the integral part of Industry 5.0. These vehicles are attaining significant importance nowadays because of their applications in consumer electronics. However, there may be numerous privacy issues in such an infrastructure. Nevertheless, neural computing-driven security solutions have played a significant role in overcoming the security and privacy issues of ai-driven intelligent flying vehicles. Motivated by these facts, this article presents a neural computing-based access control protocol for ai-driven flying vehicles to prevent all possible security threats. A detailed security evaluation of the devised protocol is done formally using the extensively recognized Real-Or-Random (ROR) oracle model. Similarly, the informal security analysis ensures the resistance of our scheme against various potential attacks under the assumptions of CK and DY adversarial models. Moreover, detailed performance scrutiny of the devised protocol with various existing schemes shows the robustness and supremacy of our scheme in terms of computation, communication overhead, and security attributes. © 1975-2011 IEEE.

关键词： Authentication

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An Enhancemnt of Low-Light Images Using Hierarchical Pyramid Attentive Network and Hierarchical Feature Fusion 2

An Enhancemnt of Low-Light Images Using Hierarchical Pyramid...

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2nd International Conference on Data science and Information System, ICDSIS 2024

作者： Suma, K.G. Nagendar, Y. Habelalmateen, Mohammed I. Revanasiddappa Halvi, Srinivas Vit University School Of Computer Science And Engineering Dept. Of Ai & Ml Andhra Pradesh India Sr University School Of Computer Science And Engineering Telangana Warangal India The Islamic University College Of Technical Engineering Department Of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute Of Technology Department Of Aiml Bengaluru India Dayananda Sagar College Of Engineering Department Of Medical Electronics Engineering Bengaluru India

ISBN: (纸本)9798350381665

The development of the low-light images assists efficiently in high visioned tasks which often fails in the poor illumination conditions. Most of the old methods, applied has enhanced the low-lighted degrade images which resulted in the undesirable enhancement including the intensive noise, blurred details and slanted color. This paper is used for finding alignment to an end-to-end image along with enhancement of the perceptual information by pixel wise. To reduce the differences in style, Hierarchical Pyramid Attentive Network (HPANet) was used, which results in the betterment of the illuminations and the reveal exposed information's. After aligned images, a hierarchy pyramid enhancements subnetwork is used for optimizing the quality of the image, which helps for removing the noises, results in enhancement low light images. A Hierarchical Feature Fusion (HFF) module is proposed which results in the high resolution of the images in perceptual qualities. Extensive experiments have also performed that effectiveness of HPANet-HFF methods on various datasets have outdone the existing methods in reproduction of the color. According to the results, the proposed HPANet-HFF obtained MAE and RMSE of 106.5 and 181.5 which is superior over other existing architectures. © 2024 IEEE.

关键词： Cell proliferation

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Blockchain based Security Management of Internet of Medical Things (IoMT) using Homomorphic Encryption using Metaheuristics with a Deep Learning Model 2

Blockchain based Security Management of Internet of Medical ...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Santiago, S. Prasanna, B.T. Qureshi, Kashif Subash, K. Trichy India Jss Science and Technology University Department of Computer Science and Engineering Mysuru India Department of Data Science Ai India Department of Data Science Trichy India

ISBN: (纸本)9798350384277

The introduction of the Internet of Medical Things (IoMT), has drastically transformed the worldwide landscape of healthcare delivery. The proliferation of IoMT devices in healthcare systems brings new issues in guaranteeing the security and privacy of sensitive patient data. We must secure a substantial amount of sensitive data from modification since IoMT is a high-volume, continually developing data domain. Since then, blockchain (BC) technology has been recognised as a distributed ledger that can manage a huge number of intelligent transactions and increase data security at the same time. A comprehensive resource management system may be offered by Blockchain-based security mechanisms with specific customisation and additional levels of authentication and verification. It has functioned brilliantly as a crucial component of the Bitcoin cryptocurrency. This work creates a blockchain-based Internet of Medical Things security management system using homomorphic encryption (HE). Key generation shows how the DOA may enhance cryptographic system security and efficacy. The IoMT-based HE-DOA system provided in this study employs blockchain to secure data exchange and diagnostic models. HE-DOA allows safe data transmission and diagnostic models with BC aid in IoMT. The recommended technique includes data collecting, encryption, and categorization. Blockchains (BCs) securely store data, and IoMT devices are crucial to early data collection. For data encryption, the dingo optimisation algorithm (DOA) may generate the key. Performance experiments and comparison with similar protocols show that the recommended procedure may enhance PSNR, accuracy, and computational time by 19.34%, 95.78%, and 1.887ms, respectively. Comparing HE-DOA to prior studies shows its scalability and durability. © 2024 IEEE.

关键词： Distributed ledger

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Integrating Renewable Energy Sources into Smart Grids with an Aggregator-Based Energy Management System for Efficiency and Resilience 7

Integrating Renewable Energy Sources into Smart Grids with a...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Kumar, B. Santhosh Devi, Anusuya Sharma, Smita Bhavana, Munugapati Babu, B. Sankara Singh, Navdeep Institute of Aeronautical Engineering Department of Cse Ai and Ml Telangana Hyderabad India New Horizon College of Engineering Department of Applied Science Karnataka Bangalore India Amity University Amity School of Engineering and Technology Uttar Pradesh Greater Noida India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India Griet Department of Computer Science and Engineering Bachupally Hyderabad India Lovely Professional University Punjab Phagwara India

ISBN: (纸本)9798350350067

Integrating renewable energy sources (RESs) into smart grids (SGs) is challenging due to the intermittent and unpredictable nature of the sources, which are often the sun or wind. Additionally, it is important to think about how to get customers involved in the electricity sector so that we can lower energy prices globally. According to this article, an energy management system (EMS) may help the SG run more efficiently. The EMS aggregates distributed energy resources to make SG market participation easier. Integrating demand-side management (DSM) with active management strategies (AMS) has financial and environmental benefits as it increases the use of renewable energy sources while decreasing consumer energy costs. By incorporating their preferences and costs into market processes, energy supply/demand and distribution system operators (DSOs) may respond to real-time price signals. This has the potential to make the grid more adaptable and resilient. To verify the effectiveness of the proposed EMS, a 23-bus, 11-kV transmission network is used. © 2024 IEEE.

关键词： Clean energy

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