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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Vembarasi, K. Thotakura, Vishnu Priya Senthilkumar, S. Ramachandran, L. Lakshmi Praba, V. Vetriselvi, S. Chinnadurai, M. E.G.S. Pillay Engineering College Department of Electronics and Communication Engineering Nagapattinam India Srk Institute of Technology Department of Electronics and Communication Engineering Vijayawada India E.G.S. Pillay Engineering College Department of Computer Science and Engineering Nagapattinam India

ISBN: (纸本)9789380544519

White Spot Syndrome Virus (WSSV) epidemics have seriously harmed penaeid shrimp aquaculture all over the world. There remains an absence of information concerning these complicated viral-host interactions, despite significant attempts to describe the virus, the circumstances that cause infection, and the processes of infection. This understanding is required to develop reliable and efficient treatment strategies for WSSV. Mechanisms for segmenting and categorizing images offer a method for extracting features from images based on their objects. Those certain objects are produced using an image segmentation technique in which segments are formed by grouping together pixels with similar spectral properties that are close to one another. The area of interest on any underlying image is protected by image segmentation, a crucial step before actual analysis is recommended in any image processing methodology. In fact, the effectiveness of the segmentation algorithm used will have a big impact on how accurate any image processing performs. This study proposes a typical segmentation technique for segmenting shrimp variability by using essential Canny-GLCM (Gray Level Co-occurrence Matrix) features with a simple Artificial Neural Network (ANN) model. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Artificial Neural Network segmentation algorithm shrimp White spot syndrome virus

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Colon Cancer Detection Using A Lightweight-CNN With Grad-CAM++ Visualization 3

Colon Cancer Detection Using A Lightweight-CNN With Grad-CAM...

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3rd International Conference on Advancement in Electrical and Electronic engineering, ICAEEE 2024

作者： Tasmim Bakchy, Sagor Chandro Peyal, Hasibul Islam Hoshen, Md. Rifat Siddique, Md. Abu Ismail Electrical & Computer Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh Electronics & Communication Engineering Hajee Mohammad Danesh Science & Technology University Dinajpur Bangladesh Electronics & Telecommunication Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh

ISBN: (纸本)9798350388282

Colon cancer is caused by cells that proliferate in the colon, a part of the large intestine. Early detection of cancer may make treatment easier and save many lives each year. The goal of this study is to identify improved approaches to diagnosing colon cancer rapidly and correctly. It intends to accomplish this through the use of a computer program known as a lightweight and efficient Convolutional Neural Network (CNN), which operates quickly and requires little computational power. The method may aid doctors in detecting colon cancer more successfully than earlier. The proposed model outperforms prior efforts with an amazing validation accuracy of 99.48\%. In our study, we employed 10,000 images from two separate classes to distinguish colon cancer cells from normal colon cells. The suggested model performs admirably, as indicated by its remarkable precision, recall, and F1-score, all of which are greater than 99\%, as well as its perfect Area Under Curve (AUC) score of 100\%. The parameters in the suggested model are lower than those in the current transfer learning models. We also compared our novel CNN model to the industry-recognized MobileNetv2 transfer learning standard to demonstrate our method's remarkable effectiveness. To illustrate the model's decision-making process, Gradient-weighted class activation mapping, or Grad Cam++, has been used. © 2024 IEEE.

关键词： Convolutional neural networks

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Cardiacnet: Cardiac Arrhythmia Detection and Classification Using Unsupervised Learning Based Optimal Feature Selection with Custom CNN Model

Informatica (Slovenia)

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Informatica (Slovenia) 2024年第2期48卷 147-158页

作者： Srinivas, Kalyanapu Ch, Vijayalakshmi Borra, Subba Reddy Raju, K. Srujan Rao, Ganga Rama Koteswara Satyanarayana, K.N.V. Kumar, Pala Mahesh Department of Artificial Intelligence and Data Science Seshadri Rao Gudlavalleru Engineering College Andhra Pradesh Gudlavalleru India Department of Electronics and Communication Engineering St. Peter’s Engineering College Maisammaguda Telangana Hyderabad India Maisammaguda Telangana Hyderabad India Department of Computer Science and Engineering CMR Technical Campus Kandlakoya Medchal Road Telangana Hyderabad India Department of Computer Science and Information Technology Koneru Lakshmaiah Education Foundation Guntur Andhra Pradesh Vaddeswaram India Department of Electronics and Communication Engineering Sagi Rama Krishnam Raju Engineering College Andhra Pradesh Bhimavaram India SAK Informatics Telangana Hyderabad India

The irregularity in the heartbeats caused cardiac arrhythmia, which resulted in serious health problems. This cardiac arrhythmia is monitored by electrocardiogram (ECG) signals. As a result, an accurate and timely analysis of ECG data can prevent serious health problems. However, the conventional manual prediction systems and artificial intelligence (AI) methods failed to detect cardiac arrhythmia because they failed to extract the deep salient features from the ECG dataset. So, this research work implements a model named CardiacNet, which is used to identify and classify cardiac arrhythmias from an MIT-BIH-based dataset. Initially, the pre-processing operation is performed to remove the non-linearities from the dataset. Then, unsupervised machine learning algorithm-based principal component analysis (UML-PCA) is used to extract the features of the pre-processed dataset. Further, the optimal feature selection operation is carried out using improved Harris Hawk's optimization (IHHO), which is a naturally inspired model. Moreover, a customized convolutional neural network (CCNN) model performs the classification of various cardiac arrhythmia diseases using IHHO features. The simulation results show that the proposed CardiacNet resulted in an accuracy of 97.57%, sensitivity of 98.29%, specificity of 97.97%, F-measure of 97.40%, precision of 98.66%, Matthew's correlation coefficient (MCC) of 98.17%, dice of 98.96%, and Jaccard of 97.12%. The performance comparisons show that the proposed CardiacNet resulted in improved metrics over all existing methods. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Convolutional neural networks

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Analysis and Detection of Fake News Using Machine Learning 3

Analysis and Detection of Fake News Using Machine Learning

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Sathish Kumar, P. Suthanthiradevi, P. Arul Stephen, C. Ebenezer Abishek, B. Sivakumar, S. Mathiyarasu, M. Computer and Communication Engineering Rajalakshmi institute of technology Chennai124 India Data Science and Business Systems School of Computing SRM institute of Science and Technology Kattankulathur Chennai603203 India Electronics and Communication Engineering Vels Institute of science Technology and Advanced studies Chennai117 India Electronics and Communication Engineering Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Avadi Chennai600062 India Electronics and Communication Engineering BIHER Chennai73 India Computer and Communication Engineering Rajalakshmi institute of technology Chennai India

ISBN: (纸本)9798350372120

The Fake news becomes a more critical issue nowadays, it may reduce the trust of the public about the particular information. This project focuses on the advancement of fake news detection model using machine learning. The main goal of this project is to develop, analyze a model that can effectively classify the news articles based on their Subject or contents. Natural preprocessing Algorithm and features such as Stemming, Tokenization, Stop word and TF-IDF Vectorization are used to train the machine learning model. Then the Machine learning Algorithms likeLogistic Regression and Random Forest are used to classifying the dataset which gives 98% accuracy and 99% of accuracy score. For this Project we have taken a dataset of 22413 Real news and 23481 Fake News from the online resources. This model has a potential to reduce the impact of fake news by grant the promotion of accurate and reliable information in a society. © 2024 IEEE.

关键词： Machine learning

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A Perspective Analysis of Optical Biosensors in Machine Learning Applications 18

A Perspective Analysis of Optical Biosensors in Machine Lear...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Kumar, B. Naveen Panduranga Rao, M.V. Sharan, Preeta Varun, K.R. Kallam, Suresh Kumaresan, M. Faculty of Engineering and Technology Department of Computer Science and Engineering Bengaluru India The Oxford College of Engineering Department of Electronics and Communication Engineering Bengaluru India The Oxford College of Engineering Department of Mechanical Engineering Bengaluru India

ISBN: (纸本)9789380544519

Optical biosensors, a novel invention at the intersection of biotechnology and optics, are revolutionizing the field of biomedical diagnostics. Optical biosensors act by identifying variations in light's wavelength, phase, polarization, or intensity caused by interactions between a sensing element and the target analyte. Optical biosensors outperform other sensor types in sensing applications due to their quick, accurate, and extremely sensitive results. When evaluating optical sensors, total power, core power, efficient area, and indices are the most crucial variables to consider. Recent years have seen notable advancements in machine learning (ML), a branch of artificial intelligence (AI). This review investigates the complementary nature of optical biosensors and machine learning methods. Here, the potential utility of ML for optical biosensors is explored in detail. The advantages and drawbacks of the most popular machine learning methods are listed based on an analysis of the optical biosensing data. It highlights the possible effects of machine learning on improving the sensitivity, selectivity, and adaptability of optical biosensing devices, and it summarizes current advancements in the field. Finally, the study considers the challenges and limitations that associated with current optical biosensing techniques. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Machine learning optical biosensor photonic crystal surface plasmon resonance surface-enhanced Raman scattering

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Breast Cancer Prediction Using Artificial Intelligence Technology 4

Breast Cancer Prediction Using Artificial Intelligence Techn...

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4th International Conference on Advances in Electrical, Computing, communication and Sustainable Technologies, ICAECT 2024

作者： Akila, V. Christaline, J. Anita Srm Institute of Science and Technology Vadapalani Dept. of Electronics and Communication Engineering Chennai India Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350343670

The aim of the project is to compare the performance of four different machine learning algorithms for breast cancer prediction such as decision tree, logistic regression, XG boost, and CAT boost. We used a dataset of patient medical records containing various clinical factors to train and test the algorithms. Accurate diagnosis and early detection are essential for enhancing patient outcomes. Data mining is a prominent tool in the healthcare industry for processing massive amounts of data. To examine massive amounts of complicated medical data, researchers use a variety of data mining and machine learning approaches. The use of these strategies can help medical practitioners forecast the onset of breast cancer. © 2024 IEEE.

关键词： Algorithms Breast cancer Data mining Machine Learning

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Safe and Reliable Live-Streaming Traffic Monitoring System using OpenCV and Yolov8

Safe and Reliable Live-Streaming Traffic Monitoring System u...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Bisht, Ashmit Sen, Kuheli Jain, Neeraj Sharma, Ravi Pragya Shivahare, Basu Dev Galgotias University Scse Department of Computer Science & Engineering Greater Noida India Galgotias University Department of Electrical Electronics & Communication Engineering Greater Noida India

ISBN: (纸本)9798331508685

Traffic monitoring is critical for avoiding fatal accidents on the roads as well as essential for ensuring that traffic rules are followed. An effective traffic monitoring system is made possible by cutting-edge computer vision technology, raising the standard of urban traffic safety and lowering the possibility of traffic accidents. The field of urban safety management will benefit greatly from this creative idea. This paper aims to create an automated traffic vehicle monitoring system using an object detection algorithm known as You Only Look Once Version 8 (YOLOv8). The model's reliability has been enhanced by the use of the YOLOv8 framework for successful vehicle object detection, speed estimation within streaming videos, as well as for license plate recognition. YOLOv8 model is used to identify vehicle objects in every single frame. The model accurately detects vehicles, while the license plate recognition module extracts the text from diverse plate formats. The study's conclusions are important because they could lead to the development of a real- time, highly accurate, and reasonably priced traffic monitoring system. Road safety might get significantly improved by this research. © 2025 IEEE.

关键词： Video streaming

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FPGA Implementation of XOR-MUX based Full Adder and Truncated Multiplier for Signal Processing Applications 15

FPGA Implementation of XOR-MUX based Full Adder and Truncate...

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15th International Conference on Computing communication and Networking Technologies, ICCCNT 2024

作者： Pavithara, P. Raghapriya, N.R. Dinesh, P.M. Gowtham, S. Viji, D. Kavin Kumar, K. Chandrasekaran, Gokul Department of Electronics and Communication Engineering Kongu Engineering College Perundurai Erode638060 India Department of Computer Science and Engineering Erode Sengunthar Engineering College Thuduppathi638057 India Department of Electronics and Communication Engineering Sona College of Technology Salem636005 India Department of Electrical and Electronics Engineering KSR College of Engineering Tiruchengode637215 India Department of Artificial Intelligence and Data Science Erode Sengunthar Engineering College Thuduppathi638057 India Department of Electronics and Communication Engineering Karpagam Institute of Technology Coimbatore India

ISBN: (纸本)9798350370249

There has been considerable advancement in the digital world, particularly in image processing applications, in recent years. The most important requirements are area efficiency, minimal delay, low power consumption, and fast speed. Due to the arithmetic operations of adders and multipliers, digital devices are prone to have increase in the size of logic because they take up more space in VLSI implementation. The proposed method of XOR-MUX based full adder and truncated multiplier concentrates on reducing the delay and area. Replacing normal multipliers with truncated multipliers computes n bit output instead of 2n bit outputs that is, the MSB bit of the truncated multiplier are retained whereas the LSB bits are truncated thereby preserving the information. This is suitable for certain applications of FIR filters that do not concentrate on lower bit length. In this project, the conventional full adders are replaced by XOR-MUX based full adders and are utilized in truncated multipliers which results in area reduction by 1 2 . 6 6 % and delay reduction by 11.68 % with a modest increase in power consumption. This reduction in truncated multipliers contributes towards the design of high performance FIR filters in signal processing applications. ©2024 IEEE.

关键词： FIR filters

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A machine learning approach for skin lesion classification on iOS: implementing and optimizing a convolutional transfer learning model with Create ML

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International Journal of computers and Applications 2024年第8期46卷 666-685页

作者： Benedek Szabo, Aron Katona, Jozsef Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary Department of Instrumentation and Automation Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Obuda University Budapest Hungary Department of Informatics GAMF Faculty of Engineering and Computer Science Kecskemét Hungary

The integration of machine learning (ML) into mobile applications presents unique challenges, particularly in resource-constrained environments such as iOS devices. Skin lesion classification is a critical task in dermatology, where accurate and efficient diagnostic tools can significantly aid in early detection of malignant lesions. This study aims to implement a machine learning-based iOS mobile application and develop a binary classification model for skin lesion images to determine whether a lesion is malignant. The research utilized Create ML to develop a convolutional neural network optimized for iOS, employing a transfer learning approach. A logistic regression model was cascaded with the convolutional neural network N to enhance classification accuracy. The model's performance was assessed through various validation metrics, ensuring its robustness and efficiency within the constraints of mobile hardware. A curated dataset from the International Skin Imaging Collaboration archive was used for training and testing. The model achieved an accuracy of 92%, a precision of 90%, a recall of 93%, and an F1-score of 91% in classifying skin lesions. These metrics validate the model's efficacy in identifying malignant lesions. Data curation involved collecting, labeling, and preparing a dataset from publicly available sources, ensuring the inclusion of diagnostically relevant features. The final model was integrated into an iOS application using Core ML and Vision frameworks. The developed application demonstrates reliable performance in classifying skin lesions with high accuracy on iOS devices. The inclusion of comprehensive performance metrics justifies the efficacy of the proposed approach. Future work will explore enhancements in model architecture and object detection capabilities to further improve diagnostic precision and application usability. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Image recognition

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Optimizing Packet Delivery in Underwater Wireless Sensor Networks: A Comparative Study of Genetic Bee Colony and Ant Colony Algorithms 9

Optimizing Packet Delivery in Underwater Wireless Sensor Net...

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9th International Conference on communication and electronics Systems, ICCES 2024

作者： Kannappan, A. Kolagani, Nagesh Kandaswamy, Vijay Anand Vishnupriyan, J. Saveetha School of Engineering Simats Department of Electronics and Communication Engineering Chennai India Saveetha School of Engineering Simats Department of Computer Science and Engineering Chennai India Chennai Institute of Technology Department of Electrical and Electronics Engineering Chennai India

ISBN: (纸本)9798350377972

Physically Underwater communication adopt the bio inspired genetic bee colony formed network communication structures. Underwater environment condition communication is established between the Transmitting node and in between several clustering relay nodes to reach receiver nodes. Transmitting nodes are placed on sea bed areas at certain depth and clustering relay nodes are placed in various underwater depth conditions dipping the sensor nodes. Also, underwater battery life is very hard and battery replacement is a very tough task. Hence, an alternate solution of optimization battery capacity utilization is used. This optimization algorithm improves Packet delivery ratio by comparing genetic bee algorithm with Ant colony algorithm. Hence, equivalence of two algorithms implemented in underwater conditions. The genetic bee colony Algorithm produces better output results more than 4 to 6 percentage compared to ant colony parameter output. The genetic bee colony algorithm offers a maximum clustering node technique compared to ant colony algorithm. Its greater support for saving energy consumption extending battery life. © 2024 IEEE.

关键词： Ant colony optimization

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