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Energy, Materials and Communication engineering (ICEMCE), International Conference on

作者： P. Sivaprakash K. S. Nisha R. Jagadish Vijay D. Madhivadhani E. Maheswari M. Sasikumar Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamilnadu India Department of Electrical and Electronics Engineering Amrita College of Engineering and Technology Nagercoil Tamil Nadu India Faculty of Electronics and Communication Engineering Kings Engineering College Chennai India Department of Electrical and Electronics Engineering Sri Sai Ram Institute of Technology Chennai India Department of Electrical and Electronics Engineering C. Abdul Hakeem College of Engineering & Technology Tamil Nadu India

Vehicle ad hoc networks (VANETs) present a unique mix of challenges and opportunities for routing protocols because to the semi-organized organization of means of transportation that faces limits of road structure and laws, as well as obstructions that limit physical access in urban environments. The dependability and scalability of routing protocols in big metropolitan VANETs are actively being explored. Furthermore, ANFIS-GWO Optimization approaches are used to increase routing sustainability and availability in VANETs by clustering vehicles beside one other based on connection locations and relative velocity, resulting in the widespread building of hierarchical network topologies. The transmission range demonstrates that the ANFIS-GWO (Adaptive Neuro-Fuzzy Inference System- Grey Wolf Optimization) is significantly more efficient than the ANFIS and GWO. End to End delay, Energy Consumption, Packet Loss and Throughput evaluation is contrasted with conventional approaches like FUZZY, ANN and ANFIS GWO Respectively. Throughput evaluation is contrasted with conventional approach ANFIS GWO is 650kbps.

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Investigating the Benefits of Text Mining for Information Analysis

Investigating the Benefits of Text Mining for Information An...

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Paradigm Shift in Information Technologies with Innovative Applications in Global Scenario (ICPSITIAGS), IEEE International Conference on

作者： Vaishali Singh Venkadeshwaran K Gaurav Jha R. Arunadevi Yogesh K. Sharma Maheswaran S Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Mechanical Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Karnataka India Department of Computer Science & Engineering Vivekananda Global University Jaipur India Department of Computer Science and Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Computer Engineering Vishwakarma Institute of Information Technology Pune India Department of Electronics and Communication Engineering Kongu Engineering College Erode Tamil Nadu India

ISBN: (数字)9798350329773

ISBN: (纸本)9798350329780

Textual content mining is the manner of extracting significant information and insights from large volumes of unstructured textual information. Many businesses are beginning to recognize the capacity of text mining in gaining effective insights and presenting greater accurate statistics. This paper will talk the ability blessings of using text mining strategies in data analysis. It's going to present quite number possible applications, such as herbal language processing, sentiment evaluation, subject matter modeling, and predictive analytics. It also evaluation the numerous technical techniques and algorithms used for textual content mining, together with lateens semantic indexing, and will look at how text mining can be used to guide a range of various commercial enterprise targets. Sooner or later, the paper will address how text mining can help pick out useful styles and traits in unstructured information, and the way it may be utilized in tandem with conventional data processing methods to achieve higher effects. This paper will finish by way of analyzing the capacity challenges that may be encountered when using text mining and how ability dangers can be mitigated.

关键词： Text mining Analytical models Text recognition Semantics Predictive models Market research Prediction algorithms

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Dielectrophoretic based Microfluidic for nanoparticles 'viruses' separation 3

Dielectrophoretic based Microfluidic for nanoparticles 'viru...

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3rd Novel Intelligent and Leading Emerging Sciences Conference, NILES 2021

作者： Abdelhamid, Hamdy Khalaf, Ibrahim Hussien, Ahmed Abdelbaset, Reda Ajman University Electrical and Computer Engineering Engineering and Information Technology department Ajman United Arab Emirates Cairo University Faculty of Engineering Electronics and Communications Engineering Department Cairo Egypt Helwan University Faculty of Engineering Biomedical Engineering Department Cairo Egypt Cairo Egypt

ISBN: (纸本)9781665421577

The objective of this paper is to provide a microfluidic platform that may be useful to manipulate and characterize different submicron particles such as latex spheres as well as viruses (E.g. SARS-COV-2) through the concept of dielectrophoresis. The significance of recent research towards microfluidics-based diagnostic chips is apparent in health care, in our homes, and also very prominently in the fight against the COVID 19 pandemic: Coronavirus disease 2019 (COVID-19) is a newly emerging human infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), early diagnosis and management are crucial for containing the outbreak. Here, we report a microfluidic device for separating mixture of different viruses. Using appropriate microfluidic channels, flow velocity, microelectrode arrays and voltage settings, particles can be trapped or moved between regions of high or low electric fields. The magnitude and direction of the dielectrophoretic force on the particle depends on the effective dielectric properties of fluid and particles, so that a heterogeneous mixture of particles can be separated to produce a more homogeneous population. In this paper the controlled separation of nanoparticles is demonstrated. With upper and lower electrode arrays, it is shown that different types of submicron latex beads can be spatially separated. The separation occurs because of differences in magnitude and direction of the dielectrophoretic force and drag force on different populations of particles. © 2021 IEEE.

关键词： Coronavirus

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FPGA based High Bandwidth LDPC using Channel Coding Technique

FPGA based High Bandwidth LDPC using Channel Coding Techniqu...

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International Conference on Edge Computing and Applications (ICECAA)

作者： S. Yuvaraj P. Latha E. Malarvizhi Balaji Madhavan Department of Electronics and Communication Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering R.M.K. Engineering College Chennai Tamil Nadu India Department of Electronics and Communication Engineering St.Joseph's College of Engineering Chennai Tamil Nadu India Department of Computer Science and Engineering Agni College of Technology Chennai Tamil Nadu India

Error-correcting codeword's using Low-Density Parity Check (LDPC) have been the subject of many studies in the communications field. Because of excellent error correction performance, low-complexity calculations, and appropriateness for parallel hardware design, this has also become typical digital modulation schemes in various protocols. Meanwhile, significant work has gone into creating LDPC decoders that can run on Field-Programmable Gate Array (FPGA) devices instead of Application-Specific Integrated Circuit (ASIC) systems due to their faster processing speeds and parallel processing capabilities. However, it is difficult to compare and even more difficult to implement the FPGA-based LDPC encoder solutions that are available in the open literature. A corrected signal decoding method with several design optimizations that lower the expenses of supporting several different codes The decoder's development findings show that it can achieve better bandwidth utilization than earlier flexible FPGA-based LDPC decoders while still meeting the requisite amount of extensibility and satisfactory error-correcting effectiveness.

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Efficient Communication and System Model for Delay Minimization in UAV-Assisted IoT Networks

Efficient Communication and System Model for Delay Minimizat...

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Frontier Technologies and Solutions (ICFTS), International Conference on

作者： Sangeetha V N. Janardhana Rao V. Subashini Rajasekhar Manda Amneh Alamleh Shilpa Bhimrao Sahare Department of AI&DS Sri Sai Ram Engineering College Chennai Dept of MBA QIS College of Engineering and Technology Ongole Andhra Pradesh Dept of Electronics and Communication Engineering Rajalakshmi Institute of Technology Chennai Dept of ECE QIS College of Engineering and Technology Ongole India Department of Computer Science Faculty of Information Technology Zarqa University Jordan University of Business and Technology - UBT Jeddah SA Dept of ME Yeshwantrao Chavan College of Engineering Nagpur India

ISBN: (数字)9798331502768

ISBN: (纸本)9798331502775

An ECSMDM is an efficient Unmanned Aerial Vehicle (UAV) communication and system model that utilizes UAPs as mobile relays to facilitate data transfer across IoT networks. By optimizing the ECSMDM protocol, factors such as packet size and throughput optimization are considered alongside energy efficiency (energy savings) and routing overhead. The purpose of UAVs is to provide a weak direct link between nodes. The Age of Information (AoI) metric ensures information is current. The UAV-BS adjusts its position to improve A2G channel connectivity, considering LOS, NLOS factors, and UAV energy usage. ECSMDM surpasses existing protocols like DSVO, OFDC, and IBUR in all aspects of measurement. The ECSMDM protocol achieves the highest packet delivery ratio of 91.07%, indicating superior network reliability, and the highest network throughput of 567.94 Kbps, reflecting better data transmission. It also records the lowest average latency of 116.36ms, which highlights its responsiveness, and the highest energy efficiency of 405.36 joules, indicating significant saving features. ECSMDM has the lowest routing of 312.54 packets, showing reduced network congestion and better performance. The ECSMDM protocol's performance enhancement in UAV-assisted data transmission highlights its potential to enhance network reliability, efficiency, and effectiveness in IoT networks.

关键词： Protocols Throughput Routing Autonomous aerial vehicles Energy efficiency Data models Delays Reliability Relays Optimization

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Sensitivity of QRS Detection Accuracy to Detector Temporal Resolution

Sensitivity of QRS Detection Accuracy to Detector Temporal R...

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2021 Computing in Cardiology, CinC 2021

作者： Heryan, Katarzyna Reklewski, Wojciech Szaflarski, Andrzej Ordowski, MacIej Augustyniak, Piotr Miskowicz, Marek AGH University of Science and Technology Department of Measurement and Electronics Krakow Poland AGH University of Science and Technology Faculty of Computer Science Electronics and Telecommunications Krakow Poland AGH University of Science and Technology Department of Biocybernetics and Biomedical Engineering Krakow Poland

ISBN: (纸本)9781665479165

The reliable evaluation of the QRS detection algorithm requires comparability and reproducibility. Although it is commonly accepted to test QRS detection accuracy by standard binary classification parameters, much less attention is paid to the temporal accuracy of the detector. A variety of temporal tolerance values are used in the literature, ranging from 60 ms to 160 ms, which sometimes result in comparison of algorithms with different temporal resolutions. This paper addresses a problem of the dependence of the accuracy of QRS detection algorithms represented by detection error rate, sensitivity, and positive predictive value, on the temporal resolution of the detection defined by Detector Temporal Tolerance (DTT). In this work, the classification statistics achieved for three state-of-the-art low complexity algorithms in a broad range of DTT (from 160 ms to 8 ms) for the entire standard MIT-BIH Arrhythmia Database are compared with the performance of the Pan-Tompkins algorithm. The analysis shows that along with decreasing DTT value, the classification statistics for R-peak detection algorithms deteriorate faster or slower with a rate characteristic for a given algorithm. In addition, the algorithms change their positions in the detection accuracy ranking with changing DTT value. The performed analysis proved that DTT is an integral parameter of QRS complex detection that determines the reproducibility of test results and fair comparative study. © 2021 Creative Commons.

关键词： Classification (of information)

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FT-FEDTL: A fine-tuned feature-extracted deep transfer learning model for multi-class microwave-based brain tumor classification

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computers in Biology and Medicine 2024年 183卷 109316-109316页

作者： Hossain, Amran Islam, Rafiqul Islam, Mohammad Tariqul Kirawanich, Phumin Soliman, Mohamed S. Department of Computer Science and Engineering Dhaka University of Engineering & Technology Gazipur Gazipur1707 Bangladesh Faculty of Engineering and Built Environment Department of Electrical Electronics and Systems Engineering Universiti Kebangsaan Malaysia Selangor Bangi43600 Malaysia Department of Electrical Engineering Faculty of Engineering Mahidol University Salaya Phuttamonthon Nakhon Pathom73170 Thailand Department of Electrical Engineering College of Engineering Taif University P.O. Box 11099 Taif21944 Saudi Arabia Department of Electrical Engineering Faculty of Energy Engineering Aswan University Aswan81528 Egypt

The microwave brain imaging (MBI) system is an emerging technology used to detect brain tumors in their early stages. Multi-class microwave-based brain tumor (MBT) identification and classification are crucial due to the tumor's patterns and shape. Manual identification and categorization of the tumors from the images by physicians is a challenging task and consumes more time. Recently, to overcome these issues, the deep transfer learning (DTL) technique has been used to classify brain tumors efficiently. This paper proposes a Fine-tuned Feature Extracted Deep Transfer Learning Model called FT-FEDTL for multi-class MBT classification purposes. The main objective of this work is to suggest a better pathway for brain tumor diagnosis by designing an efficient DTL model that automatically identifies and categorizes the MBT images. The InceptionV3 architecture is utilized as a base for feature extraction in the proposed FT-FEDTL model. Thereafter, a fine-tuning method is applied to the additional five layers with hyperparameters. The fine-tuned layers are attached to the base model to enhance classification performance. The MBT data are collected from two sources and balanced by augmentation techniques to create a total of 4200 balanced datasets. Later, 80 % images are used for training, 20 % images are utilized for validation, and 80 samples of each class are used for testing the FT-FEDTL model for classifying tumors into six classes. We evaluated and compared the FT-FEDTL model with the three traditional non-CNN and seven pretrained models by applying an imbalanced and balanced dataset. The proposed model showed superior classification performance compared to other models for the balanced dataset. It attained an overall accuracy, recall, precision, specificity, and Fscore of 99.65 %, 99.16 %, 99.48 %, 99.10 %, and 99.23 %, respectively. The experimental outcomes ensure that the proposed model can be employed in biomedical applications to assist radiologists for multi-c

关键词： Deep neural networks

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Assessing IQE for Full Duplex Relaying Schemes With Unidentified Modulation Forms

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IEEE Communications Letters 2025年

作者： Mostafa, Hala Marey, Mohamed Almustafa, Khaled Mohamad Princess Nourah bint Abdulrahman University College of Computer and Information Sciences Department of Information Technology P.O. Box 84428 Riyadh11671 Saudi Arabia Misr University for Science and Technology Faculty of Engineering Department of Electronics and Communications Engineering Egypt Department of Electrical and Computer Engineering Hawally32093 Kuwait Kuwait

This study tackles the issue of in-phase and quadrature-phase error (IQE) in full-duplex amplify-and-forward relaying (FDR) systems. This problem is analyzed within the framework of orthogonal frequency division multiplexing (OFDM) transmissions. We propose a creative approach to calculate the IQE occurring at all terminals, assuming that the modulation type is unknown. Moreover, the channel impulse responses among different nodes are incorporated into the IQE parameters, avoiding the necessity for decoupling, as done in prior research. The proposed algorithm draws on the duplication characteristics associated with FDR transmissions to obtain maximum-likelihood estimates of the pertinent parameters. Comprehensive simulations validate the efficacy of the proposed algorithm, demonstrating significant improvements in estimation performance relative to the leading algorithms. © 1997-2012 IEEE.

关键词： Orthogonal frequency division multiplexing

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Transforming Business Management Practices for Increased Production through Deep Learning and Cloud Computing integration

Transforming Business Management Practices for Increased Pro...

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Advancement in electronics & Communication engineering (AECE), International Conference on

作者： P. Santhosh Kumar Anupam Singh Soniya Shaik Farhana Amar Choudhary Susmi Biswas Department of Information Technology Ramapuram Faculty of Engineering and Technology Srm Institute of Science and Technology Chennai India Department of Computer Science and Engineering Graphic Era Hill University Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Department of Management Sanskar College of Engineering Ghaziabad India Department of Computer Science and Engineering CVR College of Engineering Hyderabad India Department Electronics and Communication Engineering Alliance College of Engineering and Design (ACED) Alliance University Bengaluru India Department of Management Science Haldia Institute of Management MAKAUT India

ISBN: (数字)9798350364729

ISBN: (纸本)9798350364736

This research is vital to meet the need for improved predictive maintenance in manufacturing, thus enabling predictive operations and minimizing expensive downtime. The aim is to assess and compare different machine learning models like Artificial Neural Networks (ANN), Support Vector Machines (SVM) and Decision Trees( DT ) with respect to predicting maintenance requirements using historical sales data, social media trends, and machinery sensor readings. The methodology included gathering a dataset of 3,200 records from the industry and then the pre-processing phase to compute the preprocessing operations like data cleaning, normalization and feature selection. The various models were then trained and tested, using 70% of the data for training to generalise the model, with the rest of the unseen test set consisting of the remaining 30%. The models were evaluated based on performance metrics (accuracy, precision, recall, F1 score and Dice Similarity Coefficient or DSC). Results showed the proposed ANN model as the most accurate and efficient prediction mechanism in comparison with precision, recall and F1 score for remaining useful life (RUL) predictions. The SVM model also worked well, with an accuracy of 95.45% but low rates of false positives and negatives. Although the DT model was informative, its performance (93.34% accuracy) was less robust than that of our RF ensemble trainer. This research demonstrates the use of these findings to be applied in real-time maintenance prediction by ANN. This research would help industries and other manufacturing sectors to reduce downtime, achieve optimal planned maintenance scheduling and improve production efficiency.

关键词： Support vector machines Accuracy Computational modeling Artificial neural networks Production Predictive models Data models Real-time systems Maintenance Manufacturing

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Deep Learning Innovations in Diagnosing Diabetic Retinopathy: The Potential of Transfer Learning and the DiaCNN Model

arXiv

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arXiv 2024年

作者： Shoaib, Mohamed R. Emara, Heba M. Zhao, Jun El-Shafai, Walid Soliman, Naglaa F. Mubarak, Ahmed S. Omer, Osama A. Abd El-Samie, Fathi E. Esmaiel, Hamada School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore Electrical Engineering Department Aswan Faculty of Engineering Aswan University Aswan81542 Egypt Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh11671 Saudi Arabia Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia

Diabetic retinopathy (DR) is a significant cause of vision impairment, emphasizing the critical need for early detection and timely intervention to avert visual deterioration. Diagnosing DR is inherently complex, as it necessitates the meticulous examination of intricate retinal images by experienced specialists. This makes the early diagnosis of DR essential for effective treatment and the prevention of eventual blindness. Traditional diagnostic methods, relying on human interpretation of these medical images, face challenges in terms of accuracy and efficiency. In the present research, we introduce a novel method that offers superior precision in DR diagnosis, compared to these traditional methods, by employing advanced deep learning techniques. Central to this approach is the concept of transfer learning. This entails using pre-existing, well-established models, specifically InceptionResNetv2 and Inceptionv3, to extract features and fine-tune select layers to cater to the unique requirements of this specific diagnostic task. Concurrently, we also present a newly devised model, DiaCNN, which is tailored for the classification of eye diseases. To validate the efficacy of the proposed methodology, we leveraged the Ocular Disease Intelligent Recognition (ODIR) dataset, which comprises eight different eye disease categories. The results were promising. The InceptionResNetv2 model, incorporating transfer learning, registered an impressive 97.5% accuracy in both the training and testing phases. Its counterpart, the Inceptionv3 model, achieved an even more commendable 99.7% accuracy during training, and 97.5% during testing. Remarkably, the DiaCNN model showcased unparalleled precision, achieving 100% accuracy in training and 98.3% in testing. These figures represent a significant leap in classification accuracy when juxtaposed with existing state-of-the-art diagnostic methods. Such advancements hold immense promise for the future, emphasizing the potential of our propos

关键词： Deterioration

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