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Novel deep recurrent neural structure with Bayesian distributed backpropagation for biomaterial model involving ferro–copper/blood nanofluids

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European Physical Journal Plus 2024年第1期139卷 1-37页

作者： Khan, Maryam Pervaiz Mukhtar, Roshana Chang, Chuan-Yu Chaudhary, Naveed Ishtiaq Shoaib, Muhammad Raja, Muhamad Asif Zahoor Department of Computer Science and Information Engineering Graduate School of Engineering Science and Technology National Yunlin University of Science and Technology Yunlin 64002 Taiwan Department of Computer Science and Information Engineering National Yunlin University of Science and Technology Yunlin 64002 Taiwan Future Technology Research Center National Yunlin University of Science and Technology Yunlin 64002 Taiwan AI center Yuan Ze University Taoyuan 320 Taiwan

The aim of the presented study is to portray the numerical treatment for biomaterial fluid flow models (BFFMs) involving magnetized ferro and copper nanoparticles dispersed in blood between two rotating disks by exploiting the knacks of layered recurrent neural networks with Bayesian distributed backpropagation (LRNN-BDB). Mathematical formulation of BFFMs is governed with PDEs that are renovated into nonlinear ordinary differential system by operating suitable transformations. The synthetic data are acquired numerically by simulated the implicit backward differentiation formulas to measure the impact of porosity permeability, Darcy–Forchheimer, and Joule heating for BFFMs involving both ferro and copper nanomaterial mixed with blood as a base fluid. The supervised computing paradigm of LRNN-BDB is executed on acquired datasets, and outcomes of LRNN-BDB are regularly overlapping with numerical observations for each variant of BFFMs with negligible error. The stupendous knacks of LRNN-BDB procedures are efficaciously depicted through mean squared error-based iterative convergence plots, adaptive controlling metrics of learning, error distribution statistics on histogram illustrations, and regression measures for exhaustive experimentation on BFFMs. © 2024, The Author(s), under exclusive licence to Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature.

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Enhancing Human-Robot Interaction through Natural Language Processing and Speech Recognition Techniques

Enhancing Human-Robot Interaction through Natural Language P...

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International Conference on Computational and Characterization Techniques in engineering & sciences (CCTES)

作者： Thotakura Veeranna K. Pavan Kumar Mahadeva Aruna Amit Verma Thanu Kurian Department of CSE(AI&ML) Sai Spurthi Institute of Technology B. Gangaram India Department of Electronics & Communication Engineering (ECE) K.S.R.M College of Engineering(Autonomous) Kadapa India Computer Science and Engineering Sai Spurthi Institute of Technology B. Gangaram India Department of Computer Science and Engineering Dr. K N Modi University Newai Rajasthan India Department of AI &ML New Horizon College of Engineering Bangalore India

ISBN: (数字)9798350364699

ISBN: (纸本)9798350364705

The rapid development of artificial intelligence (ai) has resulted in the gradual integration of robots into individuals' everyday lives and professional environments. The use of image recognition employing the robot's human-machine interface technology has become widespread. However, there are still some unresolved problems with the methods used for interaction between robots and machines, such as natural language processing (NLP) and speech recognition. Thus, building on previous research in the field, this work suggests a novel approach to robot-machine interaction by integrating the perceptron lexicon analysis model with the transfer dependence syntactic analysis model. To enhance interaction performance, researchers need to delve further into language-based human-machine interaction systems. An experimental evaluation of the structural perceptron model reveals a recall rate of 82%, an F1 value of 83%, and a testing accuracy of 96%. A maximum analysis of data speed of 751K/s is achieved using the data transfer dependence syntax analysis model. With superior robustness and reaction sensitivity, the novel technique for robot-machine interaction achieves a 93% accuracy rate in comparison to previous methods that have been tested in simulations. To conclude, research methodologies provide the foundation, both theoretically and practically, for better human-machine cooperation and enhanced robot interaction skills)

关键词： Human computer interaction Analytical models Adaptation models Accuracy Human-machine systems Speech recognition Syntactics Robot sensing systems Natural language processing Testing

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Neuromorphic Computing Architectures for EnergyEfficient Edge Devices in Autonomous Vehicles 3

Neuromorphic Computing Architectures for EnergyEfficient Edg...

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3rd IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Pradhan, Rahul Gummadi, Annapurna Tanusha, P. Sowmya, Gandla Vaitheeshwari, S. Ponni Valavan, M. Gla University Department of Computer Science and Engineering Uttar Pradesh Mathura India Hyderabad India Kl University Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram India Nri Institute of Technology Department of Computer Science and Engineering Andhra Pradesh Vijayawada India Veltech Multitech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Computer Science Chennai Avadi India K. Ramakrishnan College of Engineering Department of Ai & Ds Tamil Nadu Trichy India

ISBN: (纸本)9798350361186

Autonomous vehicles hold the promise of significantly reducing road accidents caused by human error, such as distracted driving or impaired judgment. However, the widespread adoption of autonomous vehicles faces challenges, including regulatory frameworks, liability concerns, ethical considerations, and the need for comprehensive infrastructure updates. This research focuses on the development of neuromorphic computing architectures tailored for energyefficient edge devices in autonomous vehicles. The significance of this study lies in addressing the escalating computational demands of real-time data processing in autonomous driving scenarios while mitigating the inherent power constraints of edge computing environments. The conventional approaches in autonomous vehicle computing, often centralized and powerintensive, pose challenges in terms of energy consumption and real-time responsiveness. The proposed neuromorphic computing architectures leverage spiking neural networks and event-driven processing to emulate the brain's efficiency, offering a novel solution to enhance both computational performance and energy efficiency. The research addresses prior issues related to the power-hungry nature of traditional computing paradigms in autonomous vehicles and emphasizes the need for more sustainable and responsive edge computing solutions. The novelty of this research lies in the integration of neuromorphic principles into edge devices, enabling onboard processing that is not only energy-efficient but also well-suited for the sporadic and dynamic nature of real-world driving environments. This study paves the way for advancements in autonomous vehicle technology, promising enhanced energy efficiency, reduced latency, and improved overall system performance. © 2024 IEEE.

关键词： Energy efficiency

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Employee and Client Analytical Tool 11th

Employee and Client Analytical Tool

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11th International Conference on Intelligent Computing and Applications, ICRTC 2023

作者： Gupta, Manan Rastogi, Sanchit Sharma, Puneet Arora, Deepak Department of Computer Science and Engineering Amity University Uttar Pradesh Noida India School of Computer Science and AI SR University Telangana Warangal India Department of CSE Amity University Uttar Pradesh Lucknow Campus India

ISBN: (纸本)9789819717231

Today, data analytics has become quite useful for everyone. Using the right tools, decision-makers can extract different insights of data which could help to optimize various parameters. It can help to recognize several patterns from the given loads of information. These kinds of insights can be quite useful to increase the overall performance of any company. According to this research and looking at the future scope of data science and analytics field, our team has used Qliksense software for creating an analytical tool known as "E&C AniTool" which is an advanced kind of dashboard consisting of various features and parameters for doing advance evaluation and analysis of any company data. This analytical tool can help manage the big data on a daily basis for any company using various visualization techniques which can be quite useful for any company. Qliksense is a software for visualizing data and discovering many products/tools that allow us to create user-friendly and communal visualizations that lead us to meaningful processes and decisions. The main reason to use Qliksense is that it can be accessed anywhere, anytime, and through any device. It is a modern data analytical software which is being used by many multinational corporations. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Visualization

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A Detailed Structured Monitoting System for ICU Patient Mortality rate by using ai Algorithm 5

A Detailed Structured Monitoting System for ICU Patient Mort...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

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

ISBN: (纸本)9798350383522

The complexity and seriousness of diseases in ICU patients, though, make it difficult for medical practitioners to identify high-risk patients who may face irreversible harm. With ANN (Artificial Neural Network), this study will provide an ICU admission prediction model to help identify high-risk patients at an earlier stage. After collecting and analyzing extensive data from various perspectives including demographics, medical history, vital signs, test results and treatment modalities, exploratory data analysis has been conducted. These are used as markers on how statistical techniques and machine learning algorithms can be used to predict mortality among critically ill patients. Consequently, the parameters are designed within the ANN model to create a predictive model that will be able to effectively classify those at higher risk. Advanced machine learning algorithms together with modern tools such as deep learning techniques will aid in the prompt analysis of complex information for accurate forecasts by this predictive model. The ANN model's training set will include a large dataset of ICU patients so that it can become reliable and valid. This makes the predictive model developed capable of accurately identifying high risk cases expeditiously © 2024 IEEE.

关键词： accuracy Artificial Neural Networks (ANN) exploratory data analysis high-risk patients ICU patient admission medical technology mortality outcomes periodic updates prediction model

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Applying Multi-Layer Perceptrons (mlP) for Demand Forecasting in Supply Chain Management 2

Applying Multi-Layer Perceptrons (MLP) for Demand Forecastin...

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

作者： Ankam, Sreejyothsna Durgadevi, G. Kumar, Rakesh Mounika, Bathula Alsalami, Zaid Niranjanamurthy, M. GRIET Bachupally Department of CSBS Telangana Hyderabad India New Prince Shri Bhavani College of Engineering and Technology Department of ECE Tamil Nadu Chennai600073 India GLA University Department of Computer Engineering & Applications Mathura India CMR Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India College of Technical Engineering Department of Computers Techniques Engineering The Islamic University Najaf Iraq BMS Institute of Technology and Management Department of AI and ML Karnataka Bangalore560064 India

ISBN: (纸本)9798350368109

Demand forecasting plays a significant role in supply chain management and business operational effectiveness. This paper focuses on the use of Multi-Layer Perceptrons (mlP) in demand forecasting which is one of artificial neural networks due to its proved ability to model non-linear patterns. This typology suggests a detailed approach which embraces data gathering, data preparation, model building and model assessment. The mlP model is compared with other models including ARIMA, Random Forest and LSTM models. Therefore, the use of mlP model indicated improved performance in terms of RMSE and MAPE in comparison to traditional approaches. Although LSTM has slightly higher performance, it is relatively more simpler and faster to implement mlP which results to better accuracy and fewer computations. It is identified that historical sales data, seasonal factors and economic factors are some of the most important features that affect the accuracy of the forecast. The use of mlP during the implementation of the project proves the efficiency of the proposed approach and the obtained results. This paper reveals the possibility of mlPs in improving the part of demand forecasting in the supply chain management process and suggested ways that could increase opportunities for better supply chain management through the management of inventory. Further research can be dedicated to the elaboration of the combined approaches utilizing the advantages of the notable methodologies for sales forecasting.. © 2024 IEEE.

关键词： Demand response

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Federated Learning for Residential Load and PV Production Forecast 13

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

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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

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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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A Hybrid Deep Learning Framework for Efficient Network Intrusion Detection Systems using Data Augmentation

A Hybrid Deep Learning Framework for Efficient Network Intru...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Priya Shirley Muller K Madhavi Naga Durga Saile K B Prasanthi B Jayaram Goski Sathish Department of Mathematics Sathyabama Institute of Science and Technology Chennai India Department of Mathematics Mallareddy Engineering College (Autonomous) Hyderabad India Department of Computer Science and Engineering (AIML& IoT) VNR Vignana Jyothi Institute of Engineering and Technology Hyderabad India Departmentt of Computer Science (AI&ML) CVR College of Engineering Hyderabad India Department of Computer Science and Engineering RMK Engineering College Gummidipoondi India Department of Information Technology St.Martins Engineering College (Autonomous) Hyderabad India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Because of their critical position as the initial barrier against harmful actions, intrusion detection systems (IDSs) rely substantially on the efficacy of cybersecurity in protecting corporate communication. There are several limits to machine learning approaches that be solved by utilizing other deep learning architectures, even though they are widely used for intrusion detection. In addition, unbalanced datasets frequently make it difficult to evaluate the suggested models, which prevents a thorough evaluation of the models' effectiveness. This study seeks to tackle these difficulties by improving the performance of numerous deep-learning(DL) structures for IDS using data augmentation approaches. The experimental results shown that even a basic CNN-based architecture may produce very efficient network-attack-detection, but more complicated designs only managed to show slight performance increases. The results show that cybersecurity frameworks can easily incorporate the suggested deep learning-based intrusion detection approaches, which improves the capacity to identify and counteract complex network threats. The outcomes of this research exhibit that the accuracy of intrusion detection models is highly dependent on the dataset's quality and quantity, with augmented CIC-IDS-2017 showing an accuracy of up to 92%.

关键词： Deep learning Accuracy Communication systems Network intrusion detection Complex networks Data augmentation Integrated circuit modeling computer crime

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Optimized Task Scheduling in Diverse Distributed Systems with a Hybrid Genetic Algorithm Approach 3rd

Optimized Task Scheduling in Diverse Distributed Systems wit...

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3rd International Conference on Intelligent Systems and Sustainable Computing, ICISSC 2023

作者： Muthusamy, Ramesh Kumar Kadu, Swati Ayyasamy, Karthikeyan Pichuka Veera Venkata Satya, Srinivas Arumugam, Ramachandran Ammasai, Viswanathan Sengan, Sudhakar Department of Information Technology V.S.B. College of Engineering Technical Campus Tamil Nadu Coimbatore642109 India Department of AI&DS AISSMS IOIT Pune411001 India Department of Computer Science and Engineering Panimalar Engineering College Tamil Nadu Chennai600123 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522502 India Department of Computer Science and Engineering Saveetha Engineering College Tamil Nadu Chennai602105 India School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli627012 India

ISBN: (纸本)9789819783540

In Heterogeneous Distributed Systems (HDS), efficient task scheduling (TS) across diverse resources remains a pressing challenge. This paper introduces a Hybrid Genetic Algorithm (HGA) as a solution, blending heuristic techniques with the evolutionary strengths of Genetic Algorithms (GAs) to optimize Task Scheduling (TS). Traditional algorithms often fall short in balancing computational speed with solution quality, especially in large-scale environments. This proposed HGA addresses this by leveraging domain-specific heuristics to provide a promising starting point for the GA's optimization process. The TS dilemma has been successfully defined by an all-encompassing logical system, which ensures the accuracy of the design of algorithms. In comparison tests, the HGA frequently comes out on top, demonstrating less makespan and a more excellent distribution of tasks among the resources available. Also, resources with more efficient processing capabilities will be provided priority in this recommended heuristic method, which focuses on tasks with higher computational demands. The findings of the present investigation demonstrate that the HGA may transform TS approaches by providing high-quality solutions while increasing performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Scheduling algorithms

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