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3rd International Conference on Electronics and Renewable systems, ICEARS 2025

作者： Chinnasamy, P. Khan, Patan Feridoz Sasikala, C. Swathi, Mavalluru Kumar, M. Prema Yadav, Ajay Kumar Kalasalingam Academy of Research and Education School of Computing Department of Computer Science and Engineering Tamilnadu India Mohan Babu University School of Computing Department of Computer Science and Engineering Andhra Pradesh Tirupati India Dept of Cse Andhra Pradesh Ananthapuramu India Audisankara College of Engineering and Technology Andhra Pradesh India Dept. of Ece Andhra Pradesh Bhimavaram India United Institute of Management Department of Computer Application Prayagraj India

ISBN: (纸本)9798331509675

With open connectivity and emergent computing, the Industrial Internet of Things (IIoT) combined with cloud computing resources provides significant breakthroughs in the field of industrial automation. These developments lead to the evolution of supplemental services, which improve automation in the production and industrial sectors. This research proposes novel technique in smart manufacturing system based on cloud computing with IIoT in energy efficiency using machine learning. Here the smart manufacturing process has been analysed based on IIoT with cloud computing network, where the energy efficiency is analysed using virtual machine-based reinforcement markov chain based linear regression. Experimental analysis has been carried out in terms of latency, energy efficiency, task response time, accuracy. proposed technique obtained 97% of Latency, 95% of Energy efficiency, 98% of Accuracy, 94% of Task response time. © 2025 IEEE.

关键词： Smart manufacturing

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A Novel Collaborative Framework for Cloud-Based Cognitive Services 4

A Novel Collaborative Framework for Cloud-Based Cognitive Se...

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4th International Conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Sharma, Vinod Rathore, Rachna Maheshwari, Divya Pandey, Abhishek Juneja, Ashwani Kumar Chauhan, Yuvraj Singh Slp University Dept of Cse Gwalior India Jiwaji University Dept. of Mathematics Gwalior India Mangalayatan University Dept. of Computer Engineering and Applications Aligarh India Usha Martin University Dept. of Engineering and Applied Sciences Ranchi India Vidyapeeth University Dept. of Journalism & Mc Lingayas Faridabad India Chandigarh University Dept. of Cse Chandigarh India

ISBN: (纸本)9798350364729

Cognitive Radio (CR) is transforming wireless communications by optimizing spectrum usage. CR users can access the spectrum only when primary users are inactive, requiring reliable spectrum sensing. Cooperative spectrum sensing, where data from multiple nodes is shared with a central hub, has emerged as the most effective method, surpassing local detection in dealing with challenges like noise uncertainty and channel instability. This study explores two fusion algorithms for merging local spectrum data: hard decision fusion (HDF) and soft decision fusion (SDF). While SDF offers better detection capabilities, it demands excessive bandwidth. In spectrum allocation, a complex NP-hard problem, optimization methods, especially swarm intelligence inspired by honey bee behavior, have been researched. To address spectrum assignment issues, a novel hybrid Deep Recurrent Neural Network (DRNN) approach is proposed. This hybrid model, incorporating 2-opt and additional search techniques, enhances spectrum utilization by overcoming slow convergence and local optima challenges. Empirical results demonstrate its superiority in optimizing spectrum allocation. © 2024 IEEE.

关键词： Swarm intelligence

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Biological age estimation from ECG using Artificial Intelligence approaches 2

Biological age estimation from ECG using Artificial Intellig...

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2nd Jordanian International Biomedical engineering Conference, JIBEC 2024

作者： Sleiman, Jana Daher, Mohamad Khalil, Mohamad Diab, Ahmad Kassem, Abdallah Lebanese University Computer and Communications Engineering Tripoli Lebanon Faculty of Science Faculty of Applied Sciences & Technology Dep. of Computer Science Dep. of Computer Science & Information Technology Tripoli Lebanon Lebanese University Electrical Engineering Tripoli Lebanon Lebanese International University Biomedical Engineering Beirut Lebanon Notre Dame University Faculty Of Engineering ECCE Dept. Zouk Mosbeh Lebanon

ISBN: (纸本)9798331539542

This paper explores the application of Artificial Intelligence (AI) techniques in healthcare, specifically focusing on electrocardiogram (ECG) data analysis and biological age estimation. The study begins with an overview of ECG as a diagnostic tool and discusses the integration of Deep Learning (DL) models, particularly Convolutional Neural Networks (CNNs), in healthcare for various applications. Previous studies reviewed in this context consistently identify CNNs as the superior model for ECG analysis and prediction tasks. The primary research investigates the use of Machine Learning (ML) algorithms to estimate biological age from ECG data, employing metrics such as Mean Absolute Error (MAE) and variance to evaluate model performance. Subsequently, DL models are implemented and compared, demonstrating superior accuracy in estimating the biological age of new patients. While both ML and DL approaches were initially explored, this paper primarily focuses on deep learning methodologies due to their demonstrated superiority in healthcare applications. The findings highlight the efficacy of deep learning over traditional ML methods, underscoring its potential for enhancing diagnostic accuracy and personalized patient care. For instance, the most accurate deep learning model, the Convolutional Neural Network (CNN), achieved a Mean Absolute Error (MAE) of 1.49 and an R2 score of 0.53, whereas the best-performing traditional ML model, Random Forest, achieved an MAE of 1.54 and an R2 score of 0.55. These results clearly indicate the advantage of deep learning models in capturing complex patterns in ECG data, leading to more precise and reliable estimations of biological age. © 2024 IEEE.

关键词： Convolutional neural networks

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Deep Learning-Based Stock Price Prediction: A Comprehensive Approach Using Bi-LSTM 4

Deep Learning-Based Stock Price Prediction: A Comprehensive ...

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4th International Conference on Ubiquitous Computing and Intelligent Information systems, ICUIS 2024

作者： Chy, Md. Kalim Amzad Masum, Abdul Kadar Muhammad Alam, Mohammed Shamsul Azad, Md. Abul Kalam Hassan, Md. Maruf Bhuiyan, Touhid International Islamic University Dept. of Computer Science and Engineering Chittagong Bangladesh Daffodil International University Dept. of Software Engineering Dhaka Bangladesh Islamic University of Technology Dept. of Business and Technology Management Gazipur Bangladesh School of It Washington University of Science and Technology VA United States

ISBN: (纸本)9798331529635

This study delves into the prediction of stock prices in the Bangladesh stock market using advanced deep learning frameworks, with a particular focus on the Bidirectional Long Short-Term Memory (Bi-LSTM) model. Traditional statistical models often struggle to effectively capture the complex, non-linear characteristics of financial data, leading to inconsistent results. The Bi-LSTM model, however, addresses this challenge by processing data in both forward and backward sequences, making it particularly suitable for analyzing the intricate dynamics of the Bangladesh stock market. A rigorous feature engineering process is undertaken, incorporating 64 distinct features that include market trends, stock-specific indicators, technical and fundamental variables, as well as crucial macroeconomic factors like gold prices and foreign exchange reserves. The dataset spans historical stock market data from 2012 to 2022, with comprehensive preprocessing steps taken to ensure data quality and consistency. Hyperparameter tuning is executed through a grid search to optimize model performance. The evaluation of the Bi-LSTM model is carried out using metrics such as Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), and the R2 score, which demonstrate its enhanced predictive power compared to traditional models. This research contributes to the growing field of deep learning in financial forecasting, offering new perspectives for stock price prediction in emerging markets, while also addressing ethical issues to minimize risks like market manipulation. © 2024 IEEE.

关键词： Mean square error

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Beam Profiling and Beamforming Modeling for mmWave NextG Networks 32

Beam Profiling and Beamforming Modeling for mmWave NextG Net...

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32nd International Conference on computer Communications and Networks, ICCCN 2023

作者： Fathalla, Efat Samir Zargarzadeh, Sahar Xin, Chunsheng Wu, Hongyi Jiang, Peng Santos, Joao F. Kibilda, Jacek Da Silva, Aloizio Pereira Old Dominion University Dept. Electrical Computer Engineering NorfolkVA23529 United States University of Arizona Dept. Electrical Computer Engineering TucsonAZ85721 United States Commonwealth Cyber Initiative Virginia Tech ArlingtonVA22203 United States

ISBN: (纸本)9798350336184

This paper presents an experimental study on mmWave beam profiling on a mmWave testbed, and develops a machine learning model for beamforming based on the experiment data. The datasets we have obtained from the beam profiling and the machine learning model for beamforming are valuable for a broad set of network design problems, such as network topology optimization, user equipment association, power allocation, and beam scheduling, in complex and dynamic mmWave networks. We have used two commercial-grade mmWave testbeds with operational frequencies on the 27 Ghz and 71 GHz, respectively, for beam profiling. The obtained datasets were used to train the machine learning model to estimate the received downlink signal power, and data rate at the receivers (user equipment with different geographical locations in the range of a transmitter (base station). The results have showed high prediction accuracy with low mean square error (loss), indicating the model's ability to estimate the received signal power or data rate at each individual receiver covered by a beam. The dataset and the machine learning based beamforming model can assist researchers in optimizing various network design problems for mmWave networks. © 2023 IEEE.

关键词： Beamforming

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Leveraging UAV Technology for Enhanced Disaster Management: Innovations and Applications

Leveraging UAV Technology for Enhanced Disaster Management: ...

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2024 IEEE International Conference on Signal Processing and Advance Research in Computing, SPARC 2024

作者： Upadhyay, Govind Murari Vats, Prashant Shanker, Surabhi Bhushan, Saloni Mittal, Sonu Mandot, Manju Manipal University Jaipur Dept. of Computer Application Rajasthan Jaipur India Manipal University Jaipur School of CSE Dept. of CSE Rajasthan Jaipur India K.R. Mangalam University School of Engineering & Technology Dept. of CSE Sohna Road Haryana Gurugram India Chhatrapati Shivaji Maharaj University Dept. of Computer Science/IT Maharashtra Panvel India Amity Institute of Information Technology Amity University Noida Dept. of CSE Uttar Pradesh India J. R. N. Rajasthan Vidyapeeth University Dept. of Computer Science/IT Rajasthan Udaipur India

ISBN: (纸本)9798350385199

Unmanned Aerial Vehicles (UAVs) have substantially advanced the sector by providing improved real-time monitoring, data collecting, and situational awareness capabilities when integrated into disaster management techniques. This study examines the creative uses of unmanned aerial vehicles (UAVs) in disaster relief, emphasising how they may speed up reaction times and increase operational effectiveness in emergency situations. With an emphasis on their usefulness in human detection, damage assessment, and coordination efforts, we propose a thorough framework that describes the design, deployment, and evaluation of UAV systems customised for catastrophe situations. The study shows how UAVs may be used to enhance processes of decision-making and enable more efficient recovery and catastrophe response through instances and quantitative evaluation. The results demonstrate how UAV technology may revolutionise conventional disaster management procedures and provide insightful information for future studies and real-world applications in the field. © 2024 IEEE.

关键词： Disaster prevention

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Enhancing Facial Recognition in Visual Prostheses using Region of Interest Magnification and Caricaturing 5

Enhancing Facial Recognition in Visual Prostheses using Regi...

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5th International Conference on computer and Applications, ICCA 2023

作者： Moneer, Hesham M. Elnabawy, Reham H. Eldawlatly, Seif German University in Cairo Digital Media Eng. and Technology Dept. Cairo Egypt Ain Shams University Computer and Systems Eng. Dept. Cairo Egypt The American University in Cairo Computer Science and Eng. Depr. Cairo Egypt

ISBN: (纸本)9798350303254

Blindness is a prevalent disability with significant personal and societal consequences. While medical advancements offer treatment options, severe damage to the retina, optic nerve, or brain may remain untreated. Visual prostheses are implantable medical devices that aim at providing limited vision to such individuals. However, such prostheses offer low spatial resolution making activities like reading, facial recognition, and navigation challenging. This work aims to enhance implantees' ability to recognize faces through real-time scene preprocessing, machine learning and computer vision techniques. Virtual-reality visual models simulating prosthetic vision were tested on normally/corrected sighted subjects, investigating the use of histogram equalization for contrast enhancement, facial region magnification, and caricaturing of facial features. Results revealed that histogram equalization with magnification increases facial recognition accuracy by 60%, distinguishability accuracy by 50%, and accuracy of seeing facial details by 90%. In contrast, adding facial caricaturing improved the accuracies by 66.66%, 25%, and 75% for recognizability, distinguishability, and seeing facial details, respectively. Consequently, the combination of visual field histogram equalization, face magnification, and optional caricaturing can be considered as a promising enhancement approach that could enhance the quality of vision perceived through visual prostheses. © 2023 IEEE.

关键词： Prosthetics

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Automated Disease Detection in Plant Images using Convolution Neural Network 1

Automated Disease Detection in Plant Images using Convolutio...

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1st International Conference on Computational Intelligence and Sustainable engineering Solution, CISES 2022

作者： Deepkiran Singh, Laxman Pandey, Mrinal Lakra, Sachin Manav Rachna University Dept. of Computer Science and Engineering Faridabad India Noida Institute of Engineering and Technology Dept. of Electronics and Communication. Engineering Greater Noida India NorthCap University Dept. of Computer Science and Engineering Gurugram India

ISBN: (数字)9781665480048

ISBN: (纸本)9781665480048

In this work, authors developed detection system for plant-disease using convolution neural network (CNN) model. The developed system was trained using healthy and unhealthy plants leaves on the dataset of 20,639 images taken in both lab and real-world environment from the open fields. The dataset used in this work includes 3 plant species with 15 dissimilar classes including some healthy leaves of plants. Performance of developed model was compared against most prominent machine learning algorithms such as LDA, KNN, and Gaussian Naïve Bayes classifier etc. On comparing the results, convolution neural network model was found comparatively more competent than other existing machine learning using CNN achieving the peak classification accuracy of 97% and then followed by LDA and KNN with accuracy of 95.7% and 94.4%, respectively. The results reveals that the developed system can effectively be utilized for disease detection and real-Time monitoring of plant crops in the open fields. © 2022 IEEE.

关键词： Neural network models

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Multi-Period Minimisation of Number of Control Actions in Distribution Optimal Power Flow Under Load and Generation Uncertainty Scenarios 15

Multi-Period Minimisation of Number of Control Actions in Di...

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15th IEEE International Conference on Industry Applications, INDUSCON 2023

作者： da Silva Muniz, João Rodrigo Asada, Eduardo Nobuhiro Dept. of Electrical and Computer Engineering São Carlos School of Engineering University of São Paulo São Carlos Brazil

ISBN: (纸本)9798350314182

This work proposes a multi-period expansion to the second-order cone programming (SOCP) model of distribution grid optimal power flow (DOPF) with discrete control variables and computationally evaluates it under load and generation uncertainty scenarios. When considering the inter-temporal relation of the variables, the original mixed-integer nonlinear programming (MINLP) DOPF model is computationally expensive and can be prohibitive in stochastic evaluation with multiple scenarios. The development of more efficient multi-period mixed-integer models has intensified. However, expanding and testing these models for scenarios under uncertainties is still necessary, preserving their feasibility and applicability. The proposed model aims to minimize the number of discrete control actions, adding MINLP constraints to the SOCP model for the temporal count of the control variables and time-varying multipliers for demand and generation limits. The model was applied in different scenarios of the IEEE 33-bus feeder and solved using the BONMIN solver. It was found that, when minimizing the number of control actions, even adding a large set of binary variables, there was no prohibitive increase in the solution time, and a significant reduction in the total number of actions relative to the original SOCP model was obtained. ©2023 IEEE.

关键词： daily optimization discrete control devices Distribution optimal power flow (DOPF) second-order conic programming (SOCP)

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Sign Language Recognition by using Spatio-Temporal Features

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Procedia computer Science 2024年 233卷 353-362页

作者： Renjith S Manazhy Rashmi Sumi Suresh M S Dept. of Computer Science and Engineering Amrita Vishwa Vidyapeetham Amritapuri 690525 India Dept. of Electronics and Communication Engineering Amrita Vishwa Vidyapeetham Amritapuri 690525 India Dept. of Computer Science and Engineering University at Bufalo New York City USA

The recognition of sign language has significant importance as an application that addresses the communication challenges faced by those within the Deaf and hard-of-hearing populations. This study introduces a methodology for the identification of sign language using spatio-temporal attributes, specifically concentrating on two distinct sign language datasets: the CSL (Chinese Sign Language) alphabet and the ArSL (Arabic Sign Language) alphabet. The study's objective is to create a powerful recognition system capable of reliably identifying sign language motions. A combination of spatio-temporal characteristics collected from video sequences of sign language motions is used in the suggested technique. These characteristics capture the spatial layout as well as the temporal dynamics of the motions, allowing the model to recognise signals with more precision. Experiments were carried out on the CSL and ArSL alphabet datasets to validate the method's efficacy. The findings of the trials are encouraging. The suggested system recognized CSL alphabet signs with an accuracy of 90.87% and ArSL alphabet signs with an accuracy of 89.46%. These high accuracy rates show the power of the spatiotemporal feature-based method for sign language identification. The system's success on two different sign languages implies that it is adaptable and useful in a wide variety of sign language applications. This study contributes to the evolution of assistive technology, making sign language recognition more accessible and efficient for those who communicate using sign language. Furthermore, the findings pave the way for additional research into spatiotemporal feature-based approaches in sign language recognition, with potential applications in real-world settings such as sign language interpretation and communication support.

关键词： Sign Language Classification Spatio-temporal features Time Distributed CNN

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