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检索条件"机构=Computer Science and Engineering Ajay Kumar Garg Engineering College"

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Analyzing Airline Sentiments: A Transformer Model Approach

Analyzing Airline Sentiments: A Transformer Model Approach

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2024 International Conference on Artificial Intelligence and Emerging Technology, Global AI Summit 2024

作者： Yadav, Aditi Chaudhary, Anu Gupta, Avdhesh Ajay Kumar Garg Engineering College Department of Computer Science & Engineering Ghaziabad India

ISBN: (纸本)9798350379716

Sentiment analysis, which is a vital component in NLP, plays an essential role across various platforms, including customer feedback classification. SA has appeared as an automated method for evaluating sentiment and emotion in textual content, enabling the extraction of relevant subjective information from reviews and notions within text-based data. This examination centers around the utilization of pioneering feeling examination procedures, explicitly using the transformer models, to unscramble the nuanced opinions implanted in aircraft audits. In the time of computerized correspondence, online surveys have turned into a basic source of criticism, mirroring travelers' encounters and judgements. Understanding and investigating these feelings can give significant experiences to carriers to upgrade their administrations and consumer loyalty. By utilizing the strength of transformer model such as BERT, RoBERTa, DistilBERT, the examination expects to achieve a distinguished degree of correctness in sentiment characterization, empowering carriers to perceive positive and negative opinions as well as the tones inside. This research seeks to provide a clear discrepancy between positive or negative feedbacks and employs an Artificial Intelligence (AI) model, text analysis, to distinguish and extract positive and negative feedback, as well as emotions from the 'Airline reviews' dataset. Through the integration of advanced AI techniques, the study aims to achieve a nuanced understanding of sentiments uttered within the dataset. This paper emphases on accurately detecting and categorizing feedback based on its positive or negative feedback, and also capturing the emotional tones implanted in the reviews. This is an experimental approach and this study is done to find out the precise and more accurate outcome from the 'Airline reviews' dataset using an effective transformer model and comparing this model's accuracy with other available models. © 2024 IEEE.

关键词： airline review dataset BERT model hugging face detection NLP sentiment analysis transformer model

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Cybersecurity in the Age of Blockchain

Cybersecurity in the Age of Blockchain

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2023 IEEE International Conference on Quantum Technologies, Communications, Computing, Hardware and Embedded Systems Security, iQ-CCHESS 2023

作者： Singh, Pancham Pant, Mamta Kansal, Mrignainy Singh, Janmejay Singh, Gaurav Jauhari, Sheradha Ajay Kumar Garg Engineering College Information Technology Uttar Pradesh Ghaziabad India Ajay Kumar Garg Engineering College Computer Science and Information Technology Uttar Pradesh Ghaziabad India

ISBN: (纸本)9798350314946

The frequency and complexity of cyber assaults have risen considerably in recent years, resulting in major financial losses and reputational harm for both corporations and people. Traditional cybersecurity measures, such as firewalls and encryption, have been insufficient in protecting against these threats. Due to a rise in cyber assaults and data breaches, there is an increased need for effective cybersecurity solutions. With its decentralized nature blockchain technology has come out as a promising option for enhancing cybersecurity. This article investigates how blockchain may be adapted to enhance cybersecurity in areas such as decentralized identity management, safe data storage and sharing, and smart contract security. The article also investigates the potential benefits and barriers of using blockchain in cybersecurity including some cases of successful blockchain implementations in the field of cybersecurity. The report contends that blockchain technology has been able to enhance cybersecurity by offering a secure and transparent platform for digital transactions and data sharing. © 2023 IEEE.

关键词： Blockchain

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Childhood Asthma Disease Prediction Using Classification Algorithms of Supervised Machine Learning 1

Childhood Asthma Disease Prediction Using Classification Alg...

引用

1st International Conference on Advanced Computing and Emerging Technologies, ACET 2024

作者： kumar, Pawan Jain, Jaishree Jaiswal, Updesh kumar Yadav, Deepika Solanki, Beerbal Ajay Kumar Garg Engineering College Department of Computer Science & Engineering UP Ghaziabad India Bharati Vidyapeeth's College of Engineering Department of Computer Science & Engineering New Delhi India

ISBN: (纸本)9798350367720

Asthma is a very dangerous lung disease in which our airways are swollen and become narrow and blocked. Air does not flow through narrow airways properly. It is an ongoing disease and does not go away, regular treatment is needed lifelong. According to the survey report, 25 million people in the United States are living with this disease and out of that 5 million are children who are asthma patients. Asthma is a life-threatening disease if don't get treatment timely, can lose your life. Early prediction of this disease among children not only provides a recovery solution for the disease but also helps to increase their lifetime. The proposed designed Intelligent system provides useful resources to the healthcare system to make accurate decisions for preventing asthma in childhood. The research paper aims to design an intelligent artificial-based machine learning classification model to predict asthma disease among children aged less than 10 years. Research also included a comparative analysis of different classification methods of machine learning like Linear regression, KNN Model, Decision Trees, Random Forests and Support Vector machines. It also helps to select the best model by analyzing the performances of these algorithms based on efficiency and accuracy. The outcomes of the research paper to study asthma prediction using different classification models and help doctors and patients to take the right decision timely. © 2024 IEEE.

关键词： Lung cancer

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IoT-Enhanced Seaside Monitoring for Early Flood Warning Systems using Intelligent Techniques

IoT-Enhanced Seaside Monitoring for Early Flood Warning Syst...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Thakur, Hardeo kumar Singh, Jagendra Verma, Bhumica Mishra, Pooja Anand, Akash Mahendran, N. School Of Computer Science Engineering & Technology Bennett University Greater Noida India Ajay Kumar Garg Engineering College Department Of Computer Science Engineering Ghaziabad India Ajay Kumar Garg Engineering College Department Of Electronics And Communication Engineering Ghaziabad India Aligarh Muslim University Department Of Civil Engineering Aligarh India M. Kumarasamy College Of Engineering Karur India

ISBN: (纸本)9798350366846

The research on IoT-enhanced seaside monitoring for early flood warning systems using intelligent techniques has demonstrated promising results in enhancing coastal resilience against flooding events. The system exhibited high levels of accuracy, reliability, sensitivity, and specificity, ranging from 87% to 93%, 81% to 87%, 86% to 91%, and 86% to 92%, respectively, across the 10 experiment trials. Additionally, the system-maintained prompt response times ranging from 4 to 6 minutes, ensuring timely issuance of flood warnings. These results underscore the effectiveness of integrating Internet of Things (IoT) technology with intelligent techniques for improving early flood warning systems in coastal areas. By leveraging advanced sensor technology and data analytics methods, the monitoring system can proactively detect potential flood risks, provide accurate alerts, and facilitate timely response actions to mitigate flood-related risks. The findings highlight the importance of investing in innovative solutions that enhance disaster preparedness and response capabilities in vulnerable coastal regions. © 2024 IEEE.

关键词： Internet of things

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Enhancing Breast Cancer Diagnosis: Stacked Neural Networks for Reducing Data Imbalance 2

Enhancing Breast Cancer Diagnosis: Stacked Neural Networks f...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Baghel, Rekha kumar, Ela Indira Gandhi Delhi Technical University for Women Computer Science & Engineering New Delhi India Ajay Kumar Garg Engineering College Computer Science & Engineering Ghaziabad India

ISBN: (纸本)9798350371055

Among female malignancies, breast cancer is still rather frequent globally. Mammography, an essential diagnostic tool, provides a non-invasive means of early detection. Machine learning models can aid in automating breast cancer detection, but they rely heavily on high-quality, balanced datasets. This research introduces an architectural framework, 'Stacked Neural Network,' addressing data imbalance in multiclass medical datasets, particularly for breast cancer diagnosis. Emphasizing the impact of imbalanced data on model performance in critical domains like medical diagnosis, the paper advocates for equitable predictions through data balance. Utilizing the Wisconsin Diagnostic Breast Cancer dataset, a Stacked Neural Network model is trained and evaluated. Data pre-processing involves cleaning, encoding, and partitioning for training and testing. The architecture comprises ReLU activation, dropout for overfitting mitigation, and a sigmoid output for binary classification. Results demonstrate impressive accuracies: 98.51% in training and 97.2% in validation for breast cancer classification. The study highlights the model's robustness in distinguishing tumor types © 2024 IEEE.

关键词： Data Analysis Deep learning Imbalanced Dataset Machine learning Modeling

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Prediction of Mortality in Liver Transplant Recipients Using Neural Network 5

Prediction of Mortality in Liver Transplant Recipients Using...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Prasad, Rajesh Kasiemobi, Offie Martins Ajay Kumar Garg Engineering College Ghaziabad India African University of Science and Technology Abuja Nigeria

ISBN: (纸本)9798350383522

Predicting mortality risk in liver transplantation is an essential procedure that can help inform medical professionals in decision-making, risk stratification, and patient selection. There are scoring systems that have been used in the past to predict mortality rate of patients, but they have some limitations. Recently, machine learning models have become useful for predicting mortality after liver transplantation. This study focused on the feed-forward neural networks (FNN) to predict the risk of death following liver transplantation. A correlation heatmap was employed to perform optimal feature interrelationships between features in the dataset and the recursive feature elimination (RFE) was used to select the most relevant features and reduce the data dimensionality, improving prediction accuracy. minmax Scale was used to standardize and scale the selected feature for robust result. The FNN model and other models were trained and validated. The performance of the FNN was compared with other state-of-the-art algorithms for a comprehensive evaluation, which includes logistic regression, decision trees, gradient boosting, and random forests. This study shows that the feed-forward neural network performed better than other models when using the RFE techniques, obtaining an impressive accuracy rate of approximately 95.5%, which was significantly higher than the accuracy rates of the other models. © 2024 IEEE.

关键词： clinical decision-making decision trees feed-forward neural networks gradient boosting Liver transplantation logistic regression machine learning models mortality risk predictive accuracy random forests recursive feature elimination risk stratification

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Disease Diagnostics in Paddy Fields: A Deep Learning Perspective

Disease Diagnostics in Paddy Fields: A Deep Learning Perspec...

引用

International Conference on Advances and Applications of Artificial Intelligence and Machine Learning, ICAAAIML 2023

作者： Jauhari, Sheradha Agrawal, Krishna Kant School of Computing Science and Engineering Galgotias University Greater Noida201308 India Ajay Kumar Garg Engineering College Ghaziabad India

ISBN: (纸本)9789819795062

This research aims to use a deep neural network model for the accurate detection of rice plant disease. In order to facilitate this, it is necessary to collect and pre-process relevant data. This data will be used to train the model, which will ultimately be deployed for testing. To achieve the desired results, it is essential to evaluate and optimize the model’s performance in accordance with the collected data. For this, a VGG-16 based deep learning model will be built to classify and identify rice plant disease. Necessary data for this research would be collected and pre-processed in order to prepare it for the model’s training. The model will then be trained using this data, and its performance will be evaluated and optimized in accordance to the desired accuracy. Subsequently, the trained model will be tested on the collected data to accurately identify and classify rice plant disease. Overall, this research aims to use a VGG-16 based model for accurately identify and classify rice plant disease. It involves the collection and pre-processing of relevant data, training the model, and subsequent deployment for testing. The performance of the model will also be evaluated and optimized accordingly. If successful, this research should ultimately result in an accurate detection of rice plant disease. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Alex net Deep learning model Mobile net Rice plant leaves disease VGG-16

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Optimizing Safe Routes with Machine Learning: A Crime Prediction System for Urban Safety 1

Optimizing Safe Routes with Machine Learning: A Crime Predic...

引用

1st International Conference on Emerging Technologies and Innovation for Sustainability, EmergIN 2024

作者： kumar, Aman Maurya, Aastha kumar, Pawan Gupta, Aman Saini, Jatin Department of Computer Science & Engineering Ajay Kumar Garg Engineering College Uttar Pradesh Ghaziabad India

ISBN: (纸本)9798350391268

In urban environments, ensuring safe routes for commuting and travel is a growing concern. This study introduces a machine learning-based crime prediction system aimed at optimizing safe routes in cities by forecasting crime trends. By utilizing historical crime data, which includes categories such as murder, rape, kidnapping, and theft, the system identifies crime-prone areas and generates safety scores for various routes. The proposed model leverages predictive analytics to assess future crime probabilities, enabling urban dwellers and authorities to make informed decisions about safe navigation. This tool is particularly useful for law enforcement to allocate resources efficiently and for residents seeking safer pathways during their daily routines. Ultimately, this crime prediction system provides a data-driven approach to enhance urban safety and community well-being. © 2024 IEEE.

关键词： crime forecasting crime mapping crime prediction data-driven decision making machine learning predictive analytics public safety route optimization safe routes urban safety

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Cotton Leaf Disease Classification Using Deep Learning based Novel Approach

Cotton Leaf Disease Classification Using Deep Learning based...

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2023 International Conference on Disruptive Technologies, ICDT 2023

作者： Pandey, Bihari Nandan Singh, Raghvendra Pratap Pandey, Mahima Shanker Jain, Sachin Ajay Kumar Garg Engineering College Department of Computer Science and Engineering UP Ghaziabad India Ajay Kumar Garg Engineering College Department of Electronics and Communication Engineering UP Ghaziabad India Bbditm Department of Information Technology UP Lucknow India

ISBN: (纸本)9798350323887

In India, one of the essential professions in agriculture. Cotton is the best crop out of several and is very important to the nation's agricultural economy. Six million farmers in India depend directly on cotton, while 40-50 million people are employed in the cotton trade and cotton processing. Over the past few decades, the cotton leaf disease has become a significant issue, causing losses in harvests, agricultural activities, and financial resources. Farmers may find it challenging to find a solution if they cannot accurately identify the characteristics of the crop's actual disorder at the outset. This study intended to develop a model to aid in the detection of cotton leaf pests and diseases. In this study, we propose a hybrid strategy based on deep learning. On a data set of 1710 images, the presented model accurately discovers Leaf Curl Disease and Fusarium wilt in cotton plants with an accuracy of 98.9%. © 2023 IEEE.

关键词： Cotton

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Analysis of Underlying Emotions in Textual Data Using Sentiment Analysis Which Classifies Text In to Positive, Negative or Neutral Sentiments 14

Analysis of Underlying Emotions in Textual Data Using Sentim...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Prajapati, Yogendra Narayan Yadav, Sandeep Sharma, Sandhya Patel, Umesh kumar Tomar, Swati Ajay Kumar Garg Engineering College Computer Science & Engineering Ghaziabad India Sgit College Computer Science & Engineering Ghaziabad India Gniot College Computer Science & Engineering Noida India

ISBN: (纸本)9798350335095

Analysis of underlying emotions in textual data can provide important insights and has been the focus of much research. Sentiment analysis is a popular technique which classifies text into positive, negative or neutral sentiments based on polarity and subjectivity. But this fails to extract actual emotions represented by text. This can be achieved by analyzing the occurrence of particular words, symbols and emojis in text. For emotion classification, machine-learning and deep-learning techniques Peng et al.(2022) are also used. These models classify textual data into only a small subset of the broad spectrum of emotions. Subtle variations such as "disappointment"and "grief"are lost and text is simply classified into "sadness". Extracting such subtle emotions from texts present some unique problems as one single sentence may represent multiple related emotions. Thus we have explored various Deep learning models based on convolution Neural NetworkO'Shea and Nash (2015) and Long Short Term Memory Staudemeyer and Morris (2019) for classifying textual data into one or more of 27 different fine grained emotions. © 2023 IEEE.

关键词： Sentiment analysis

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