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A Multistage Cascaded Neural Network Model to Identify the Severity of Epileptic Attack

A Multistage Cascaded Neural Network Model to Identify the S...

引用

2024 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems, SPICES 2024

作者： Simon, Martha Thampi, Lidiya Lilly Dept. of Computer Science College of Engineering Trivandrum India Indian Institute of Information Technology Kottayam Dept. of Computer Science Kottayam India

ISBN: (纸本)9798350376135

Epilepsy is a neurological disorder characterized by recurrent seizures, impacting the quality of life for millions of individuals worldwide. Timely and accurate detection of epileptic seizures is crucial for proper diagnosis and effective treatment. Electroencephalogram (EEG) signals provide vital information about brain activity and serve as a valuable tool for epilepsy detection. The methodology opted for this study was an enhanced multilayer feed forward neural network for automatic detection of epilepsy from EEG signals and hence predicting the severity. The authors build a multistage cascaded neural network model for achieving the same. Initially a Simple Artificial Neural Network (SimpleANN) is designed to classify EEG segments into seizure, healthy and interictal. Later a Multilayer Feed Forward Neural Network (MFFNN) is employed for detecting the severity of epileptic patients and hence can be classified into primary and secondary stages. The experimental results showed that the proposed SimpleANN classifier had provided noticeable results, and outperforms all tested classifiers. The study involves the collection of EEG data from a diverse group of patients, including those with epilepsy and healthy individuals. This automated epilepsy identifying system passes through different stages including decomposition, feature extraction for achieving a better performance model. The proposed system was tested and compared with SVM, KNN, MLP and Decision Tree under different measurement metrics. ANN has managed to give an accuracy of 80 - 90% which is significantly higher as compared to other machine learning models. The proposed work can automatically learn complex patterns from EEG data provides an advantage in identifying subtle seizure related features that may not be evident through manual analysis. Furthermore, the model offers real-time capabilities, making it suitable for continuous monitoring of patients at risk of seizures. © 2024 IEEE.

关键词： Electroencephalography

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Prediction and Management of Traffic Congestion in Urban Environments 15

Prediction and Management of Traffic Congestion in Urban Env...

引用

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

作者： Gwalani, Aryan Pai, Abhishek Padalia, Adish Bhavathankar, Prasenjit Devadkar, Kailas Sardar Patel Institute of Technology Dept. of Information Technology Mumbai India Sardar Patel Institute of Technology Dept. of Computer Engineering Mumbai India

ISBN: (纸本)9798350370249

Efficient urban traffic congestion management is paramount for ensuring smooth vehicular flow, minimizing travel time, and reducing environmental pollution. In this context, utilizing intelligent intersection technologies emerges as a promising approach to tackling traffic challenges effectively. This paper presents an in-dept. analysis of the 'Intelligent Intersection' framework, a novel system designed to optimize traffic flow at intersections through advanced sensing, data processing, and control mechanisms. The research integrates the Gated Recurrent Unit (GRU) model for urban traffic prediction, Simulation of Urban Mobility (SUMO) for realistic traffic simulations, and adaptive traffic light control algorithms. Additionally, the integration of Vehicle-to-Infrastructure (V2I) communication, particularly Dedicated Short Range Communication (DSRC), facilitates efficient coordination between vehicles and infrastructure elements. Through comprehensive experimentation and evaluation, the effectiveness of this approach in mitigating traffic congestion, reducing travel time, and promoting sustainable transportation practices is demonstrated. This research contributes to the advancement of intelligent transportation systems and lays the groundwork for future innovations in urban mobility management. © 2024 IEEE.

关键词： Travel time

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Reviewlogy: An Interface for Analyzing Sentiments 3

Reviewlogy: An Interface for Analyzing Sentiments

引用

3rd IEEE World Conference on Applied Intelligence and Computing, AIC 2024

作者： Shyam, Radhey Khanna, Shilpi Srivastava, Kavya Srivastava, Ajay Kumar Chaudhary, Ishika Bbd University Dept. of Computer Science & Engineering Lucknow India Srmcem Dept. of Information Technology Lucknow India

ISBN: (纸本)9798350384598

This paper investigates the impact of early reviews on product sales by analyzing the traits of initial reviewers on major e-commerce platforms. It segments product life-cycles into three phases: early, majority, and laggards, focusing on early reviewers who post reviews during the initial stages. The paper introduces an innovative interface for sentiment analysis to identify and predict early reviewers. Quantitative analysis reveals that early reviewers often provide more insightful comments and give higher overall ratings. Their ratings and helpfulness scores significantly affect product popularity. The methodology, tested on extensive datasets from Amazon and Yelp, extracts crucial attributes of early reviewers, including purchasing patterns and social impact. The interface employs a hybrid machine learning approach combining Naive Bayes, margin-based embedding ranking models (MERM), and support vector machines (SVMs) to predict potential early reviewers accurately. This approach balances probabilistic reasoning with robust classification, ensuring comprehensive forecasting. Evaluation metrics such as precision and accuracy demonstrate the effectiveness of the models over baseline methods. Furthermore, the study enhances the understanding of how early reviews influence consumer behavior and product success in e-commerce. The interface aids in refining product marketing strategies by enabling precise targeting of early reviewers, accelerating product adoption, amplifying sales, and optimizing marketing tactics in the competitive e-commerce landscape. © 2024 IEEE.

关键词： Support vector machines

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Skyline Algorithm based on QoS to improve Web Services: A Review 6

Skyline Algorithm based on QoS to improve Web Services: A Re...

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6th Parul International Conference on Engineering and technology, PICET 2024

作者： Barge, Yamini Patel, Tanvi Purohit, Lalit Dept. of Computer Science Engineering Parul University Vadodara India Dept. of Information Technology SGSITS Indore India

ISBN: (纸本)9798350369748

The popularity of Web Service-based systems is due to the advantage of flexibility, reusability and support for rapid system development. Web Service selection is a very important step for these web service-based systems. The skyline technique is widely used in the past for the selection of web services. In this work, an exhaustive study of Skyline-based web service selection techniques is presented. A review of the existing state-of-the-art is presented based on the approach followed for selection of web services, the advantages of using the proposed approach, the limitations of the approach and the problem addressed. © 2024 IEEE.

关键词： Web services

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Improving Fake News Detection with a Mixture of Experts: Leveraging Sentiment Analysis and Sarcasm Detection as Core Expert Models 6

Improving Fake News Detection with a Mixture of Experts: Lev...

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6th International Conference on Advancements in Computing, ICAC 2024

作者： Gunasekara, Tharindu Thihan, Disira Kodithuwakku, Sansika Dissanayake, Dinuka Fernando, Harinda Vidhanaarachchi, Samitha Sindhujan, Archchana Sri Lanka Institute of Information Technology Malabe Dept. of Software Engineering Sri Lanka Sri Lanka Institute of Information Technology Malabe Dept. of Computer Computer Systems Engineering Sri Lanka University of Surrey Dept. of Computer Science and Electronic Engineering United Kingdom

ISBN: (纸本)9798331517878

Fake news continues to proliferate, posing an increasing threat to public discourse. The paper proposes a framework of a Mixture of Experts, Sentiment Analysis, and Sarcasm Detection experts for improved fake news detection. This approach captures the emotional cues in the text through a Sentiment Analysis expert, which is based on bidirectional encoder representations from Transformers (BERT) models with sentiment vectors generated using SentiWordNet and Integrated Gradients. It combines a sarcasm detection expert based on BERT, recognizing sarcasm and its type to help classify fake news. By fusing these experts through a Mixture of Experts gateway, subtle linguistic cues often found in fake news are more effectively analyzed, leading to improved accuracy in detecting misinformation. Experimental results are presented as 96% for the Sarcasm expert with the BERT base model and 83% for the Sentiment Analysis expert with the distilled version of the BERT (DistilBERT) base model, proving the effectiveness of the proposed approach in beating traditional methods. © 2024 IEEE.

关键词： fake news detection gateway mixture of experts sarcasm detection sentiment analysis

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Pathways for Security of Embedded System: Issues and Challenges 2

Pathways for Security of Embedded System: Issues and Challen...

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2nd International Conference Computational and Characterization Techniques in Engineering and Sciences, IC3TES 2024

作者： Diwaker, Sanjai K. Chaurasia, Pawan Kumar Khaliq, Mazhar Khwaja Moinuddin Chishti Language Dept. of Computer Science and Information Technology Lucknow India Babasaheb Bhimrao Ambedkar Central University Dept. of Information Technology Lucknow India

ISBN: (纸本)9798350364699

In today's scenario, security threats are increasing day by day as technology. Security breaches not only affect financial loss but also affect the trustworthiness and integrity of the software design and embedded software development industry. These breaches directly affect the security attributes. The objective of security in an embedded system is to protect the device from vulnerable attributes. The aim of this paper, identify challenges in embedded systems applied in various applications, research gaps, and untouched areas to discover possible parameters for sustainable devices. © 2024 IEEE.

关键词： Access Control Authenticity Authorization Cyber security Device Manager Healthcare Internet of Medical Things

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A New Architecture for Decentralized Cybersecurity Mesh (DCSM) Using Blockchain technology for Distributed Networks 1

A New Architecture for Decentralized Cybersecurity Mesh (DCS...

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1st International Conference on Emerging Technologies for Dependable Internet of Things, ICETI 2024

作者： Al-Hussaini, Khalid Al-Swidi, Asalah Abdulkareem Information Technology Dept. Thamar University Dhamar Yemen Computer Science Dept. Thamar University Dhamar Yemen

ISBN: (纸本)9798331533557

Cybersecurity Mesh Architecture (CSMA) is a promising new approach to cybersecurity that has the potential to improve network and information security. As networks become more complex and widespread, detecting and responding to threats has become more difficult. It has also led to security creep that leads to complexity of management, fragmentation of visibility, and limits organizations ability to respond effectively to threats. These and similar challenges have made integration with CSMA a necessity for today's organizations. The cybersecurity mesh can be viewed as centralizing security management and distributing the application of this management, but in some completely distributed networks, such as the Internet of Things (IoT), Cloud Computing, Edge Computing, Big Data, etc., the cybersecurity mesh is not compatible with them because it applies the principle of central management. Therefore, the researchers propose, in this paper, to change the cybersecurity mesh architecture. The cybersecurity mesh will be completely distributed using blockchain technology, where the network security infrastructure will first be changed to the blockchain network. After that, blockchain technology will be integrated into each layer of the proposed cybersecurity mesh. Finally, the proposed architecture will be evaluated by ensuring complete distribution and the expected security provided by the cybersecurity mesh. © 2024 IEEE.

关键词： Blockchain technology Decentralized Cybersecurity Mesh Architecture DCSMA Distributed Networks and Technologies Zero Trust Model ZTM

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Quantitative Analysis and Forecasting in Finance: Leveraging Wavelet Packet Transform for Enhanced Market Insights

Quantitative Analysis and Forecasting in Finance: Leveraging...

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2024 International Conference on Communication, Computing and Internet of Things, IC3IoT 2024

作者： Libina, M. Kavitha, C. Kabilan, A. Ifet College Of Engineering Dept. Of Information Technology Villupuram India Manakula Vinayagar Institute Of Technology Dept. Of Computer Science Puducherry India

ISBN: (纸本)9798350352689

Finance forecasting is always known for its ever-changing and complex nature in geopolitical markets, requiring advanced analytical tools to provide valuable deep insights for financial advisors, investors, and vital decision-makers. In this study, we delve into the realm of both quantitative analysis and forecasting in finance-related markets, utilizing sophisticated techniques to enhance our understanding of the markets. Our study focuses on the innovative utilization of the Optimized Wavelet Packet Transform (WPT), an advanced tool for processing signals, to uncover complex patterns within financial time series data through quantitative analysis using Plotly's interactive line charts. It bridges the gap between quantitative analysis and forecasting. It also reaches an accuracy rate of 99.5%, which is considered the best among the other prediction models. These insights help stakeholders and advisors navigate the ever-evolving landscape of financial markets and make decisions wisely. © 2024 IEEE.

关键词： Forecasting

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Detection and segmentation of oral lesion using Mask R-CNN

Detection and segmentation of oral lesion using Mask R-CNN

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2024 Asian Conference on Intelligent Technologies, ACOIT 2024

作者： Kavyashree, C. Vimala, H.S. Shreyas, J. Dept of Computer Science UVCE Bangalore India Dept. of Information Technology MAHE Bangalore India

ISBN: (纸本)9798350374933

Early detection of oral cancer is essential for enhancing patient outcomes and preserving lives. Nevertheless, inaccurate and inappropriate diagnosis may impede the effectiveness of treatment. In recent years, deep learning techniques have assumed greater significance with the advancement of artificial intelligence, establishing themselves as a fundamental technology in the field of medical diagnosis. It automates image classification process and enhances accuracy. The study proposes Mask R-CNN (Region-based Convolutional Neural Network) for identifying the cancerous part or lesion in an image. The Photographic images are annotated and trained using mask R-CNN. With backbone as ResNet-50 extract features and used to obtain the bounding box along with the classification and segmentation mask. ROI (Region of Interest) alignment aids in enhancing the localization of objects by fine-tuning the feature maps and ROI, thereby delivering highly accurate segmentation masks. ROI align also ensures the prevention of data loss. The model provides the mean average precision (mAP) of 75.34%, precicsion of 76.45% and a recall of 73.25%. This technique enables the automated identification of cancerous regions within an image, assisting medical professionals in early detection. © 2024 IEEE.

关键词： Convolutional neural networks

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Real-Time Air Pollution Monitoring System Employing IoT 2

Real-Time Air Pollution Monitoring System Employing IoT

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Ghose, Banani Rehena, Zeenat Haldia Institute of Technology Dept. of Information Technology Haldia India Aliah University Dept. of Computer Science & Engg. Kolkata India

ISBN: (纸本)9798350327533

Modern livelihood is endangered by many challenges. Such a challenge is air pollution. To exterminate the lives of the residents from the ill effects of air pollution one has to gather the knowledge of air health of a place he/she is planning to go in. A timely response from a real-time air pollution monitoring system may be convenient in this regard. In this paper, an loT -enabled air pollution monitoring system is proposed, which is competent in collecting real-time data itself, and after data collection, it is proficient in analyzing and delivering the assessment of the enquired place's air quality with some suggestive measures to the users who place a query regarding that. Several air pollution-related sensors are deployed in this regard to capture the real-time pollutant concentrations and meteorological parameters, and the air health assessment work is accomplished using an efficient data fusion technique, which delivers the air health status of a place with the help of the current data only without using historical data. Finally, the air health status is communicated to the users via some web applications. The system is sound and smart enough to analyze the air health of a place with utmost accuracy and promptness. © 2024 IEEE.

关键词： Air pollution monitoring system Deep learning Multi-sensor data fusion Real-time

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