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A Coherent Way of Detecting Learner's Academic Emotions via Live Camera Using CNN and Deep LSTM 4

A Coherent Way of Detecting Learner's Academic Emotions via ...

引用

4th International Conference for Emerging technology, INCET 2023

作者： Rathi, Snehal Oswal, Samkit Ahuja, Ayushi Vishwakarma Institute of Information Technology Department of Computer Engineering Maharashtra Pune India

ISBN: (纸本)9798350335750

Academic Emotion Detection is fundamentally a system for detecting emotions. The system's main goal was to identify feelings expressed while attending online lectures during the COVID-19 epidemic. The topic Academic Emotion Detection using Machine Learning focuses on utilizing machine learning and deep learning to identify human face emotions in light of the shift to online *** research has a limited scope, it focuses on four academic emotions: confusion, boredom, engagement, and frustration. A person may experience a wide range of other emotions as well. Here, we have used CNN and Deep LSTM for the prediction of said four emotions and it has been observed it increases the accuracy of prediction and effectiveness. We even incorporated a portion of a questionnaire into our research to compare our results with genuine human *** Neural Network (CNN), Long-Short Term Memory (LSTM), and Recurrent Neural Network (RNN) are three different algorithms from the deep learning area that we have used in this study to examine how they operate and identify similarities and differences. © 2023 IEEE.

关键词： Emotion Recognition

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Crop Recommender System Based on Ensemble Classifiers

Crop Recommender System Based on Ensemble Classifiers

引用

2023 International Conference on Advancement in Computation and computer Technologies, InCACCT 2023

作者： Vuyyala, Voshma Reddy Kona, Michael Sadgun Rao Pusuluri, Sai Bhargavi Variganji, Swetha Nenavathu, Bhavani PVP Siddhartha Institute of Technology Department of Computer Science and Engineering Vijayawada India

ISBN: (纸本)9798350396485

Farmers are facing problems because they are unable to manage cultivation because of bad weather conditions and uneven rainfall. Thus, to reduce the problems of farmers, the latest technologies are introduced such as machine learning to implement crop recommendation systems. A wide range of classification techniques are used, and a specific model is selected based on their accuracy levels. By using feature selection techniques, the raw data is converted into a dataset which is useful for efficiently training the model with relevant data. Reducing redundant data and utilizing just the aspects that are significantly relevant in deciding the model's final output will improve the model's accuracy. The findings show that, compared to other classifiers, the ensemble approach delivers better prediction with a 99.54% accuracy rate. document is a ''live'' template and already defines the components of your paper [title, text, heads, etc.] in its style sheet. © 2023 IEEE.

关键词： Recommender systems

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Adoption of Blockchain technology in Land Registry Systems 3

Adoption of Blockchain Technology in Land Registry Systems

引用

3rd International Conference on Secure Cyber Computing and Communications, ICSCCC 2023

作者： Sah, Chandan Kumar Chandavarkar, B.R. National Institute of Technology Karnataka Department of Computer Science & Engineering Surathkal India

ISBN: (纸本)9798350300710

The adoption of blockchain technology in land registry systems has gained significant attention in recent years due to its potential to increase efficiency, transparency, and security. This paper provides an overview of the current state of land registry systems and the challenges they face, in-cluding issues related to fraud, corruption, and a lack of trust. Then it discusses how blockchain technology can address these challenges by providing a decentralized, tamper-proof, and transparent platform for recording land transactions. The paper also examines some of the existing blockchain-based land registry systems and their features, using approaches and methodologies, including the use of smart contracts and digital signatures. Finally, the paper discusses the potential benefits and limitations of adopting blockchain technology in land registry systems and provides recommendations for policymakers and stakeholders interested in implementing these systems. © 2023 IEEE.

关键词： Smart contract

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Multi-lingual Event Identification in Disaster Domain 1

引用

20th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2019

作者： Ahmad, Zishan Varshney, Deeksha Ekbal, Asif Bhattacharyya, Pushpak Department of Computer Science and Engineering Indian Institute of Technology Patna Bihta India

ISBN: (数字)9783031243370

ISBN: (纸本)9783031243363

Information extraction in disaster domain is a critical task for effective disaster management. A high quality event detection system is the very first step towards this. Since disaster annotated data-sets are not available in Indian languages, we first create and annotate a dataset in three different languages, namely Hindi, Bengali and English. The data was crawled from the different news websites and annotated with expert annotators using a proper annotation guidelines. The events in the dataset belong to 35 different disaster classes. We then build a deep ensemble architecture based on Convolution Neural Network (CNN) and Bi-directional Long Short Term Memory (Bi-LSTM) network as the base learning models. This model is used to identify event words and phrases along with its class from the input sentence. Since our data is sparse, the model yields a very low F1-score in all the three languages. To mitigate the data sparsity problem we make use of multi-lingual word embedding so that joint training of all the languages could be done. To accommodate joint training we modify our model to contain language-specific layers so that the syntactic differences between the languages can be taken care of by these layers. By using multi-lingual embedding and training the whole dataset on our proposed model, the performance of event detection in each language improves significantly. We also report further analysis of language-wise and class-wise improvements of each language and event classes. © 2023, Springer Nature Switzerland AG.

关键词： Disasters

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Leveraging Biomedical Ontologies to Boost Performance of BERT-Based Models for Answering Medical MCQs 14

Leveraging Biomedical Ontologies to Boost Performance of BER...

引用

14th International Conference on Biomedical Ontologies, ICBO 2023

作者： Sahil Kumar, P. Sreenivasa Department of Computer Science & Engineering Indian Institute of Technology Madras Chennai India

Large-scale pretrained language models like BERT have shown promising results in various natural language processing tasks. However, these models do not benefit from the rich knowledge available in domain ontologies. In this work, we propose BioOntoBERT, a BERT-based model pretrained on multiple biomedical ontologies. We also introduce the Onto2Sen system to process various ontologies to generate lexical documents, such as entity names, synonyms and definitions, and concept relationship documents. We then incorporate these knowledge-rich documents during pretraining to enhance the model’s "understanding" of the biomedical concepts. We evaluate our model on the MedMCQA dataset, a multiple-choice question-answering benchmark for the medical domain. Our experiments show that BioOntoBERT outperforms the baseline model BERT, SciBERT, BioBERT and PubMedBERT. BioOntoBERT achieves this performance improvement by incorporating only 158MB of ontology-generated data on top of the BERT model during pretraining, just 0.75% of data used in pretraining PubMedBERT. Our results demonstrate the effectiveness of incorporating biomedical ontologies in pretraining language models for the medical domain. © 2023 Copyright for this paper by its authors.

关键词： Ontology

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Analysis of Convolutional Neural Network Architectures for the Classification of Lung and Colon Cancer 4th

Analysis of Convolutional Neural Network Architectures for t...

引用

4th International Conference on Machine Intelligence and Signal Processing, MISP 2022

作者： Titoriya, Ankit Kumar Prasad Singh, Maheshwari Department of Computer Science and Engineering National Institute of Technology Patna Patna India

ISBN: (纸本)9789819900466

Cancer is the most common disease, which leads to death worldwide. Colon, prostate, breast, bladder, and lung are the common types of cancer among men and women. This work helps researchers to compose an automated classification of lung and colon cancer using histopathology images. This study compares various Convolutional Neural Network (CNN) architectures for image classification using the feature extraction method for the LC25000 dataset. In this method, a pre-trained network extracts features from the dataset, and the multi-class classification approach classifies the test images. All models show excellent performance in classification. This study achieves accuracy between 97.32% to 99.79% for lung-colon cancer image classification. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Medical imaging

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Edge Computational Integrated Improved Data Model for Adjustment and Restoration Strategy for Smart Grid Environment 1

Edge Computational Integrated Improved Data Model for Adjust...

引用

1st IEEE International Conference on Emerging Research in Computational Science, ICERCS 2023

作者： Premalatha, J. Sarojini Abirami, G. Poornima, D. Sivakumar, Gayathri Sathyabama Institute of Science & Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350359756

In the current context, integrated distribution networks of the electricity grid to provide reliability during blackouts. Self-healing systems may keep functioning and reconfigure themselves in response to external breakdowns or attacks by using the efficient strategy of restoration. An intelligent decision-making framework is necessary to ensure the system as a whole can self-reconfigure in the face of external failures and attacks. Incorrect grid modifications can pose a significant threat to the power grid's security and cause delays in network traffic and packet loss. So, in this research, Edge Computational Integrated Improved Data paradigm (EC-IDM) to deal with failures and attacks in real time. By mitigating the consequences of security worries, packet loss, latency, and overcrowded networks, edge computing has greatly increased the smart grid's capacity for efficient operation and communication. Smart Grid's posterior intelligent model (SSPIM) integration enhances the system's functionality and productivity. This research introduces an EC-IDM that can detect blackout scenarios and automatically construct a restoration strategy in real time. In EC-IDM, an enhanced hierarchical coordination mechanism and a local object optimization method are combined to optimize failures and attacks in real time over many time and space axes. Differentiating features of EC-IDM include real-time data analysis, networked simulation, smart decision support, and a wealth of other useful features. The simulation results indicate that the recommended strategy achieves best in security concerns optimizes network congestion delay, and packet loss security, increases efficiency and performance compared to the other standard ways. © 2023 IEEE.

关键词： Smart power grids

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TCI-Net: A Deep Learning Approach for Tropical Cyclone Intensity Prediction

TCI-Net: A Deep Learning Approach for Tropical Cyclone Inten...

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2023 IEEE International Conference on Network, Multimedia and Information technology, NMITCON 2023

作者： Gujral, Sahil Rao, Darshan Khanvilkar, Anuj Shah, Umit Maktum, Tabassum Ramrao Adik Institute of Technology Department of Computer Engineering Navi Mumbai India

ISBN: (纸本)9798350300826

One of the most affecting types of weather phenomena are tropical cyclones (TCs), commonly referred to as typhoons or hurricanes. Atlantic Ocean, North Pacific Ocean, Western Pacific Ocean, South Pacific Ocean, and Indian Ocean are the five oceans where hurricanes, typhoons, and tropical cyclones can form. Many techniques have been tried in the past to monitor cyclones and assess their intensity before they form, but they have always been shown to be ineffective. It is crucial to estimate TC intensity in order to forecast and decrease human loss and suffering. Recently, data scientists in addition to meteorologists have started to show interest in the task. This claim demonstrates an innovative approach based on image processing for predicting TC intensity given a meteorological geostationary satellite imagery. The Convolutional Neural Network (CNN) architecture which has to be modified in a number of ways, will be used to create the suggested cyclone intensity estimating model. The results obtained from experiments conducted on a widely accessible benchmark dataset reveal that the proposed approach stands out as one of the most precise models for estimating TC intensity. Moreover, it exhibits remarkable stability across various scenarios. These findings underscore the substantial promise of harnessing data science in the field of meteorological research. © 2023 IEEE.

关键词： Convolution

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Implementing End to End Encryption to Communication Apps 7

Implementing End to End Encryption to Communication Apps

引用

7th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2023

作者： Nalini, N. Harini, B. Nitte Meenakshi Institute of Technology Department of Computer Science & Engineering Bangalore India

ISBN: (纸本)9798350341485

This research study intends to continuously assess the security of cloud models against three types of passive attackers and one type of aggressive adversary. This study has demonstrated how some of the systems' security breaches result from the threat models and what steps are frequently taken to ensure these systems' confidentiality. This study also examines the most popular items on the market and demonstrate the degree of security. Online Social Networking (OSN) services allow users to publish "posts"that contain personal information, media, and news items. On OSN, users can create groups and send messages to one another in private conversations. A group of people can communicate privately with one another. Online discussion is another name for this. These show how these systems are open to inference attacks because of their unencrypted metadata and access patterns. And last, generic design principles for safely developing such systems. This research study defines and introduces the Virgil end-to-end encryption method, which makes use of a symmetric AES key encryption mechanism. There is now a fully functional human messaging service as a result. Instead of relying on outside programmers with questionable or undefined security standards, we created our prototype from the ground up in Java. © 2023 IEEE.

关键词： Cryptography

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An Intelligent Air Defense Recommendation Model based on Fuzzy Inference System 26

An Intelligent Air Defense Recommendation Model based on Fuz...

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26th International Conference on computer and Information technology, ICCIT 2023

作者： Islam Arif, Md. Ariful Islam, Zohirul Sharmee, Sharmin Sultana Akhtaruzzaman, M. Military Institute of Science and Technology Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350359015

Securing the nation's airspace demands the integration of an advanced air defense system. Automation technology can be incorporated to deploy anti-missile rockets, acting as a deterrent against various air-missile threats. This research aims to identify potential threats within a protected area and integrate an automatic system into the air defense measures based on detection. In the event of identifying incoming missiles, decisions will be made by gathering and validating information on specific issues through dedicated radars. Special emphasis is placed on utilizing a Fuzzy system to determine the nature of the threat, distinguishing between scrambling or surface-to-air missile launches based on distance, secondary radar input, and the speed of the airborne object. The paper provides a comprehensive comparison of defuzzification methods, demonstrating their effectiveness in modern air defense systems. The chosen method proves suitable, emphasizing its pertinence to contemporary air defense needs. The Fuzzy system offers insights into optimal times, types, and launch locations for interceptors or surface-to-air missiles (SAMs) within an integrated air defense system. The research evaluates the defuzzification process using five different methods, with MoM method yielding notably superior results. The incorporation of technology is crucial for ensuring the safety of protected areas, key installations, and the dynamic decision-making processes inherent in air defense systems, necessitating the use of fuzzy logic-based systems. © 2023 IEEE.

关键词： Air defense fuzzy inference system fuzzy logic integrated system intelligent system

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