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A Blockchain assisted fog computing framework for secure data sharing mechanism in smart home 1

A Blockchain assisted fog computing framework for secure dat...

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1st IEEE International Conference on Contemporary Computing and Communications, InC4 2023

作者： Apat, Hemant K. Sahoo, Bibhudatta Mohanty, Sagarika Mukul, Manas Barik, Rabindra K. National Institute of Technology Computer Science and Engineering Rourkela India Kalinga Institute of Industrial Technology School of Computer Applications Bhubaneswar India

ISBN: (纸本)9798350335774

The demand and popularity of Internet of Things (IoT) has been growing with exponential rate due to the rapid growth of various smart devices in the IoT ecosystem. The request generated through various smart devices is generally stochastic and unscheduled. The existing IoT-Cloud paradigm does not provide the security services efficiently and effectively. In order to provide better security services to different IoT applications domain particularly in smart home domain, it introduces the blockchain technology. So, in the present article, it proposes BlockCFS framework, which is an innovative blockchain assisted fog computing framework, as a safe datasharing mechanism for smart homes. It also illustrates the methodical sequence diagram for the BlockCFS, for registering and transmitting data from a variety of smart devices that are connected in a smart home. © 2023 IEEE.

关键词： Fog computing

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Price Comparison Using Web Scraping and Machine Learning 1

Price Comparison Using Web Scraping and Machine Learning

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1st International Conference on computer science and Emerging Technologies, CSET 2023

作者： Singh, Neelam Rana, Aryan Chaudhary, Anmol Graphic Era University Department of Computer Science and Engineering Dehradun India Graphic Era University Department of Computer Applications Dehradun India

ISBN: (纸本)9798350341737

This work aims to create a robust and user-friendly web scraping-based price comparison system tailored for e-commerce websites. By leveraging advanced web scraping techniques, the system extracts and processes product prices from various platforms. It employs data analysis methods to uncover patterns and trends, enabling users to make well-informed decisions when searching for the most competitive deals across multiple e-commerce platforms. Through its intuitive interface and visualization capabilities, the system empowers users to effortlessly navigate the vast online market and discover the best prices for their desired products. © 2023 IEEE.

关键词： Visualization

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OHDLL:Optimized Hybrid Deep Learning Model for Classification of Leukaemia images

OHDLL:Optimized Hybrid Deep Learning Model for Classificatio...

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2023 International Conference on the Confluence of Advancements in Robotics, Vision and Interdisciplinary Technology Management, IC-RVITM 2023

作者： Saju, Binju Asha, V. Tressa, Neethu Suhas, R.A. Jagesh, Shah Yash Sandhya, V. Adi Shankara Institute of Engineering & Technology Department of Computer Science - Ai Engineering Kerala Kalady India Master of Computer Applications New Horizon College of Engineering Bengaluru India

ISBN: (纸本)9798350306088

A serious haematological cancer that causes mortality and morbidity in people of all ages is leukaemia. The haematologists examine bone marrow and blood to determine Leukaemia. Techniques for manual blood testing that have been in use for a long period are frequently slow and produce less reliable diagnoses. The study proposes a hybrid model to classify leukaemia cell images. Initially, images are pre-processed which includes Image resizing, Gray scale conversion and noise removal using a combination of Weiner filter and Median filter. Further, images are enhanced using Gaussian Mixture based histogram equalization. Features of the images are extracted using Dense Convolutional neural network. An optimized ResNet101 model with improved Aquila optimization is used for classification. Performance of the models is compared with other studies in literature. The proposed model has obtained an F1 score of 98.98%, an accuracy of 98.20%, sensitivity of 98.9%, specificity of 98.1%, and precision of 98%. © 2023 IEEE.

关键词： Median filters

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Performance Comparison of Various Optimization Techniques for ECG Signals

Performance Comparison of Various Optimization Techniques fo...

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2023 International Conference on Computational Intelligence and Sustainable engineering Solution, CISES 2023

作者： Sharma, Pooja Dinkar, Shail Kumar Veer Madho Singh Bhandari Uttrakhand Technical University Computer Science and Engineering Dehradun India Gbpiet Department of Computer Science and Applications Pauri Garhwal India

ISBN: (纸本)9798350323917

Feature extraction from the ECG (Electrocardiogram) data is a most important phase for the identification of heart diseases. As a result, the value of the ECG categorization approach in the present medical world is quite high. Many machine learning programmes are available that may be used to classify and analyse ECG data. The primary flaw in most ECG classification methods is that they have non-optimized capabilities for unstructured function learning frameworks. The problem originates from the lack of methods that might accurately handle the task of ECG categorization. The use of optimisation algorithms coupled with the deep learning idea is the apparent answer among the different strategies that are now accessible. The crucial part is figuring out the right R-peaks and RR intervals. In the suggested study, performance metrics of various meta heuristic optimisation techniques i.e. Cuckoo search along with Support Vector Machine(SVM), Sine Cosine Algorithm (SCA) and Linear Adaptive Sine Cosine Algorithm(LA-SCA) are compared. To assess and train the system, signal data is used from the MIT-BIH arrhythmia database. The main goal of this paper is to obtain information about various arrhythmia classes. Considering accuracy and precision as performance parameters, the comparison of above said algorithms is presented. © 2023 IEEE.

关键词： Electrocardiograms

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Integrating Big Data Analytics in Soil Quality Assessment: Techniques, Challenges, and applications 3

Integrating Big Data Analytics in Soil Quality Assessment: T...

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3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Mohammed, Shehu Singh, Arun Hanif, Md Abu Lovely Professional University Department of Computer Applications Phagwara India Lovely Professional University Department of Computer Science and Engineering Phagwara India Department of Computer Science 365 5th Ave New YorkNY110016 United States

ISBN: (纸本)9798331515911

This review paper investigates the application of Big Data analytics, focusing on soil quality assessment, with an emphasis on the innovative benefits offered by modern data-driven techniques for improved soil management. With world food demands rising, the importance for real-time and exact soil quality monitoring has increased. Although still useful, traditional approaches to modeling are limited in their ability to capture the spatial and temporal variability of soil properties. Big Data analytics sits as the answer to the above by facilitating extensive and large volume data processing resulting in better, on-demand and scalable soil quality assessment. In the paper, we describe machine learning algorithms, remote sensing and geospatial analysis as key techniques to mine soil data at scales and resolutions unprecedented so far. We also critically evaluate the challenge of data integration (heterogeneity, scalability and multi-disciplinarity). These analytics are scalable for assessment and decision-making in precision agriculture, sustainable land management and environmental conservation. Thus, this paper synthesizes current research and practical implications in support of the need for Big Data analytics within soils and soil sciences to cover also future challenges. Future research directions are also highlighted, including the importance of improved data acquisition and algorithmic advances as well greater synergy between Big Data and conventional soil science practices.. © 2025 IEEE.

关键词： Data quality

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Attack Detection and Mitigation in IoT using SVM 2

Attack Detection and Mitigation in IoT using SVM

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2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Srivastava, Animesh Tiwari, Shweta Saini, Parveen Kumar Sawan, Vikash Dhondiyal, Shiv Ashish Graphic Era Hill University Department of Computer Science & Engineering Dehradun India University Institute of Engineering Chandigarh University Department Computer Science & Engineering Department Punjab Mohali India Chandigarh University Department of Computer Science & Engineering Punjab Mohali India Gla University Department of Computer Engineering and Applications Mathura India Department of Computer Science & Engineering Dehradun India

ISBN: (纸本)9798350325799

As the Internet of Things (IoT) increases along with the cost of sensors decrease, more and more people are interested in the concept of the Internet of Things (IoT). Denial of service (DoS) and distributed denial of service (DDoS) attacks could be launched against these IoT devices. When there are many different types of IoT devices, security risks increase. There is currently no protocol in place to guarantee the security of IoT gadgets. However, resilience only works when there is constant observation and flexible planning. This can dynamically and flexibly deal with security threats to IoT devices without putting any extra load on the IoT devices themselves. This study proposes a strategy for quickly detecting and counteracting suspicious activity and malicious attacks. In Machine Learning, both Linear SVM and Non-Linear SVM techniques are employed, but the latter yields more accurate results. The accuracy of linear SVM is 93%. However, the accuracy increases to 97.8% when non-linear SVM is used. © 2023 IEEE.

关键词： Denial-of-service attack

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An Emperical Analysis of Machine Learning Algorithms for Used Car Price Prediction System

An Emperical Analysis of Machine Learning Algorithms for Use...

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2023 IEEE Global Conference on Information Technologies and Communications, GCITC 2023

作者： Bhatt, Nirav Sanjay Nath Pandey, Trilok Reddy, Suravaram Rishabh Jayasurya, B. Dash, Bibhuti Bhusan Shekhar Patra, Sudhansu Vellore Institute of Technology School of Computer Science and Engineering Chennai India Kiit Deemed to Be University School of Computer Applications Bhubaneswar India

ISBN: (纸本)9798350308167

The market for used cars is a dynamic and ever-expanding industry, with various factors influencing the pricing of pre-owned vehicles. Accurate pricing is crucial for both buyers and sellers, as it ensures fair transactions and minimizes the risk of financial loss. This paper introduces a machine learning algorithms developed for the purpose of predicting used car prices based on a comprehensive set of features. Various machine learning (ML) techniques such as multiple linear regression, ridge regression, lasso regression, Bayesian regression, Decision tree regression, random forest regression, eXtreme Gradient Boost (XGB) regression, Light Gradient Boosting Machine (LGBM) regression, Gradient boosting regression and Multilayer Perceptron (MLP) regression. To assess the algorithm's performance, rigorous evaluation metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared (R2) score has been employed. © 2023 IEEE.

关键词： ANN Bayesian Regression Decision Tree Regression Lasso Regression LGB Linear Regression MLP Random Forest Regression Ridge Regression XGB

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Improved UNET Architecture for Effective COVID-19 Severity Prediction from Pulmonary CT Images 1

Improved UNET Architecture for Effective COVID-19 Severity P...

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1st International Conference on Innovations in Communications, Electrical and computer engineering, ICICEC 2024

作者： Ramla, M. Jasmine, R. Anita Ramesh, M. Bai, Femilda Josephin Joseph Shobana SRM Institute of Science and Technology Department of Computer Applications Chennai India SRM Institute of Science and Technology Department of Data Science and Business Systems Chennai India SRM Institute of Science and Technology Department of Computer Science Chennai India Istinye University Department of Computer Engineering Istanbul Turkey

ISBN: (纸本)9798350376517

COVID-19, is one of the deadliest pandemic hit globally crises in the recent years, continues to persist with emerging variants of concern. Due to the virus's high transmissibility, rapid and precise detection of COVID-19 is crucial for timely medical intervention. This research presents an advanced deep transfer learning approach for quantifying the severity of COVID-19 infection from CT lung images. Using computer vision techniques, lung regions were extracted from CT scans through semantic segmentation using SegNet. An enhanced U-Net architecture, integrated with the Atrous Spatial Pyramid Pooling (ASPP) technique, was employed to semantically segment the infected regions of the lungs. Based on the segmented infection areas, the severity of the infection was classified into mild, moderate, and severe. The proposed framework, which incorporates lung extraction followed by Ground-Glass Opacity (GGO) segmentation, achieves the segmentation accuracy of 96% and a prediction accuracy of 98.9%. © 2024 IEEE.

关键词： COVID-19

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Meme Expressive Classification in Multimodal State with Feature Extraction in Deep Learning 2

Meme Expressive Classification in Multimodal State with Feat...

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2nd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2023

作者： Barveen, A. Geetha, S. Faizal, M.K.Mohamed M.I.E.T. Engineering College Department of Computer Science and Engineering Trichy India University College of Engineering Department of Computer Applications Tamilnadu Trichy India

ISBN: (纸本)9798350397635

Memes are a socially interactive way to communicate online. Memes are used by users to communicate with one another on social networking sites and other forums. Memes essentially focus on speech recognition and image macros. While a meme is being created, it focuses on the semiotic type of resources that the internet community interprets with other resources, which facilitates the interaction among the internet and meme creators. Memes recreate based on various approaches, which fall under various acts such as existing speech acts. Based on the expressive face with captioned short texts, even the short text is exaggerated. Every year, meme mimicking applications are created that allow users to use the imitated meme expressions. Memes represent the shared texts of the younger generations on various social platforms. The classifications of sentiment based on the various memetic expressions are the most efficient way to analyse those feelings and emotions. HOG feature extraction allows the images to be segmented into blocks of smaller size by using a single feature vector for dimension, which characterizes the local object appearances to characterize the meme classification. The existence of specific characteristics, including such edges, angles, or patterns, is then analyzed by combining HOG features using multi-feature analysis on patches. Based upon the classification methodology, it classifies the sentiments, which tend to improve the learning process in an efficient manner. By combining a deep learning approach with a recurrent neural network, the extended LSTM-RNN can identify subtle nuances in memes, allowing for more accurate and detailed meme classification. This proposed method effectively evaluates several classification techniques, including CNN and Extended LSTM-RNN for meme image characterization. Through training and validation, Extended LSTM-RNN achieved 0.98% accuracy with better performance than CNN. © 2023 IEEE.

关键词： Feature extraction

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Analysis of Educational Recommender System Techniques for Enhancing Student's Learning Outcomes 3

Analysis of Educational Recommender System Techniques for En...

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3rd International Conference on Innovative Practices in Technology and Management, ICIPTM 2023

作者： Pal, Neeti Dahiya, Omdev School of Computer Applications Lovely Professional University Jalandhar India School of Computer Science and Engineering Lovely Professional University Jalandhar India

ISBN: (纸本)9798350336238

Recommender System was widely used in commercial websites for the past few years. These systems track past activities of customers and recommend them the relevant items. The emergence of E-learning activities over a few decades develops a variety of E-learning content available for virtual learning environments (VLE). A large amount of learning objects is present in E-learning repositories. Dealing with problems of the diversity of data, Educational Recommender System (ERS) plays a vital role in the educational sector. Educational recommender systems track the learners' past activities, know the users' preferences, assist the educators and learners, provide relevant content to learners, and enhance their learning outcomes. A personalized recommender system will intensify the learners' interest in particular content and reduce the course dropout rate. The recommender system makes the decision-making process for choosing the appropriate content easy for learners. ERS uses various approaches and technologies for assisting the learners' and helps them to run their learning process smoothly. Collaborative filtering, Content-based, and knowledge-based are the basic techniques of recommender systems. The research shows that a combination of these approaches will give more effective and efficient results. This mixing of approaches refers to the hybridization techniques. Traditional approaches with deep learning networks will improve the recommendations and provide results with higher accuracy. This paper provides how E-learning support recommender systems produce recommendations using different techniques. The technical results of recommender techniques help to find the best approach for making a recommender system in the future. © 2023 IEEE.

关键词： Recommender systems

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