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Enhanced Sentiment Analysis of Airline Twitter Review Using Hybrid Machine Learning and Deep Learning Models 1

Enhanced Sentiment Analysis of Airline Twitter Review Using ...

引用

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

作者： Premasudha, B.G. Patil, Vinayak Siddaganga Institute of Technology Department of Master of Computer Application Tumkur India

ISBN: (纸本)9798350376517

Understanding public opinions and trends requires effective sentiment analysis of social media data. This research explores advanced approaches for sentiment classification using the Twitter US Airline Sentiment Dataset, which contains 14,640 tweets. We assess the effectiveness of various models, including Long Short-Term Memory (LSTM) networks, Naive Bayes, and Support Vector Machines (SVM), along with different text vectorization techniques: Bag of Words (BoW), TF-IDF, and Word2Vec. The results demonstrate that LSTM networks achieve an impressive accuracy of 94.5%, surpassing SVM (90%) and Naive Bayes (85.6%). Among vectorization methods, both BoW and TF-IDF yield the highest AUC scores at 0.92, while Word2Vec-based techniques provide improved F1-Scores, with W2V-AVG at 0.66 and W2V-TFIDF at 0.65. These findings highlight the superior performance of LSTM and Word2Vec for accurate and balanced sentiment classification. © 2024 IEEE.

关键词： Tweets

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Secure Data Deduplication for Efficient Cloud Storage Using Blockchain Technologies 7

Secure Data Deduplication for Efficient Cloud Storage Using ...

引用

7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Chandra Sekar, M. Shanthi, H.J. Hindustan Institute of Technology and Science Department of Computer Application Chennai India

ISBN: (数字)9798350372816

ISBN: (纸本)9798350372816

Secure data deduplication for efficient cloud storage using blockchain technologies addresses the challenge of reducing storage costs and improving data management in cloud environments. By leveraging blockchain technology, this approach ensures data integrity and security during deduplication processes. The system eliminates redundant data blocks while maintaining a secure authentication mechanism to verify the integrity of stored data. Blockchain's decentralized and tamper-resistant nature enhances the trustworthiness and transparency of the deduplication process. Through the utilization of cryptographic techniques and smart contracts, the system guarantees secure data handling, ensuring that only authorized users can access and modify deduplicated data. By incorporating blockchain technology, the proposed solution enhances the efficiency and security of cloud storage operations, making it a promising approach for organizations seeking to optimize storage resources and protect sensitive information. © 2024 IEEE.

关键词： Data integrity

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Zomato ETL for Chain of Orders Using Random Forest Algorithm 7

Zomato ETL for Chain of Orders Using Random Forest Algorithm

引用

7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Baskaran, S.U. Gokila, S. Hindustan Institute of Technology and Science Department of Computer Application Chennai India

ISBN: (数字)9798350372816

ISBN: (纸本)9798350372816

In the realm of food delivery services, efficient data processing plays a crucial role in enhancing customer experience and optimizing operational workflows. This study investigates the application of Zomato ETL (Extract, Transform, Load) processes coupled with the Random Forest algorithm to analyze a chain of orders within the platform. By extracting data from various sources such as customer preferences, order history, and delivery patterns, the ETL process transforms this information into actionable insights. The Random Forest algorithm is then employed to predict customer behavior, optimize delivery routes, and personalize recommendations. The high values for accuracy, precision, recall, and F1-score indicate that the Random Forest model used in the Zomato ETL system is performing exceptionally well in classifying or predicting the target variable based on the given dataset. An accuracy of 98.77% and an F1-score of 98.43% are considered excellent scores, demonstrating the model's effectiveness in correctly identifying and predicting the instances in the dataset. This integrated approach not only streamlines order processing and delivery operations but also enables Zomato to offer a more tailored and seamless experience to its users. © 2024 IEEE.

关键词： Sales

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A Comparative Study of Logistic Regression, Support Vector Machines, and LSTM Networks for Sentiment Classification in Academic Reviews 1

A Comparative Study of Logistic Regression, Support Vector M...

引用

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

作者： Premasudha, B.G. Rampalli, Vijayalaxmi Siddaganga Institute of Technology Department of Master of Computer Application Tumkur India

ISBN: (纸本)9798350376517

In recent years, sentiment analysis has become an important tool for understanding what people think and feel about different topics, especially in education. This study looks at how well Long Short Term Memory (LSTM) networks work compared to traditional methods like Support Vector Machines (SVM) and Logistic Regression for classifying sentiment in text. By analyzing a dataset of 18,901 college reviews, which includes reviews sourced from Kaggle and additional reviews for improved dataset balance, this research evaluates how well LSTM networks can detect nuanced sentiments compared to more conventional models. The study includes steps like cleaning the data, testing and fine-tuning several machine learning methods, and measuring how accurately they capture the contextual nuances in the reviews. Findings reveal that LSTM networks outperform traditional methods, offering enhanced accuracy in capturing the emotional depth and context of the text. This study provides valuable insights into the use of advanced neural network models for sentiment analysis, highlighting their ability to improve the interpretation of user feedback. The findings suggest that incorporating LSTM networks into educational feedback systems could result in more accurate sentiment classification and contribute to improved decision-making processes. © 2024 IEEE.

关键词： Support vector regression

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Framework for classifying gait disorders and fall prevention

Framework for classifying gait disorders and fall prevention

引用

2025 International Conference on computer, Electrical and Communication Engineering, ICCECE 2025

作者： Saha, Rupa Karmakar, Koushik Biswas, Suparna Narula Institute of Technology Department of Computer Application India Narula Institute of Technology Department of Computer Science and Technology India Department of Computer Science & Engineering Makaut W B India

ISBN: (纸本)9798331513894

One of the crucial approaches that use tracking and analyzing human movement for the identification of functional disorders and illnesses of the human body is known as the gait analysis method. This method helps diagnose neurological and musculoskeletal conditions. It also tracks the effectiveness of rehabilitation. However, conventional techniques for analyzing gait frequently depend on costly items and methods like fixed lab apparatus, force plates, and motion capture devices. But new developments in wearable sensor technology and machine learning provide new methods that are cheaper, more affordable, portable, and easily accessible. It continuously tracks gait in real-world settings. In this work, the use of wearable sensors for gait analysis, which includes devices like magnetometers, gyroscopes, and accelerometers, is examined. They are integrated into gadgets like smartphones, insoles, and smartwatches and offer real-time kinematic data that machine learning algorithms can analyze. Some of the important gait parameters 1that are extracted from sensor data are stride length, cadence, and gait symmetry. Machine learning is used to process this data and use it for categorization and gait analysis. The suggested method improves patient monitoring outside of clinical settings. It supports individualized rehabilitation plans and also identifies early indicators of gait abnormalities. The use of the wearable sensor-based gait analysis may emerge as a game-changing medical tool. It offers constant, discrete monitoring and prompts intervention in gait-related disorders. This works explains it thoroughly. This report offers insights for upcoming researchers in the field by surveying the state of the art in wearable sensor-based gait analysis. © 2025 IEEE.

关键词： Wearable sensors

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Cloud service price prediction using Machine Learning Algorithm with API in the case of Amazon Web Services and Microsoft Azure

Cloud service price prediction using Machine Learning Algori...

引用

2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Dhayanandan, B. Rajeev, R. Hindustan Institute of Technology and Science Department of Computer Application Chennai India

ISBN: (纸本)9798350375237

Due to the availability of highly scalable and reasonably priced infrastructure, software, and platform services, many enterprises are moving their on-premise artificial intelligence workloads to the cloud. The study focuses on the K-Nearest Neighbors (KNN) algorithm and uses a quantitative method to build and assess a predictive model for cloud service price prediction. Pre-processed historical pricing data from Microsoft Azure and AWS is gathered, together with variables that affect the cost of cloud services. In order to generate models, the KNN algorithm is used, and an API is made to integrate real-time price predictions. Evaluation measures (MAE, MSE, and RMSE) are employed. Because cloud pricing is unpredictable, the suggested method addresses this dynamic by improving accessibility and adaptability. Findings from exploratory data analysis and features of the API show how effective the system is. This contribution involves providing customised solutions for each customer while taking ethical and practical constraints into account. All things considered, the integrated approach is a major improvement in cloud service pricing prediction, allowing for 'well-informed' budgetary and resource allocation decisions. © 2024 IEEE.

关键词： Costs

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Secure Online Product Recommendation System Utilizing Blockchain with SHA-256 Hash Generation 15

Secure Online Product Recommendation System Utilizing Blockc...

引用

15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Kumar, R. Ajith Janit, R. Sophia Department of computer application Hindustan institute of technology and science Chennai India

ISBN: (纸本)9798331300579

Blockchain-based product tracking creates anirreversible and permanent ledger of a product's whole lifespan, from conception to final delivery, revolutionizing supply chain management. This technique creates a transparent and safe system for tracking and validating product information all the way through the manufacturing process by utilizing blockchain technology. Manufacturers and distributors can ensure transparency and authenticity at every level of the supply chain by using blockchain technology to trace every step-in detail. Because blockchain is unchangeable and tamper-proof, it is the perfect tool for preventing fake goods and guaranteeing product integrity. Blockchain lowers the risk of fraud and manipulation by recording every movement and transaction in the supply chain in a decentralized, transparent manner. Consumer trust is strengthened by this increased degree of responsibility and openness, which also protects the authenticity and quality of products. Customers feel more confident when blockchain technology is used since they can be sure of the origin and caliber of the goods they buy. Improved traceability and transparency empower customers to make knowledgeable decisions and protect themselves from the dangers of fake goods. Additionally, blockchain strengthens brand loyalty and integrity by creating a marketplace that is transparent and trusting, protecting companies from the negative impacts of counterfeiting. © Grenze Scientific Society, 2024.

关键词： Blockchain

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Robust Hand Gesture Recognition Based On computer Vision

Robust Hand Gesture Recognition Based On Computer Vision

引用

2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Tharun, R. Lakshmi, I. Hindustan Institute of Technology and Science Department of Computer Application Chennai India

ISBN: (纸本)9798350375237

Research in this field has been provoked by the developing interest in human-computer interaction. Also, normal client interfacing and encounters (NUI/NUX) that make use of a user's discourse and developments have been done. In NUI/NUX, a discourse or signal acknowledgment calculation is required. Tragically, numerous acknowledgment calculations have disadvantages since of their complex engineering and long preparation times. Pre-processing, normalization, and extraction must therefore be carried out. In this thought, we planned and actualized a hand-mouse interface that utilizes Kinect to extract a user's physical highlights in genuine time, showing a special concept known as a 'virtual screen.' The hand mouse may work in a virtual zone, which is much appreciated for this virtual show. It is conceivable to outline the arrangements on the virtual show precisely to those on the genuine screen. Based on the virtual screen concept, a hand-mouse interface jams the exceptionally instinctive nature of the past;consider upgrading the accuracy of mouse operations. We tried the precision and value of theinterface with fifty volunteers, extending in age from 15 to 50. 84D44 of members in this test of instinct were able to utilize them in less than a miniature. The precision try too illustrated the accuracy of the mouse's tap, drag, and double-click capacities. Typically, a good illustration of an interface will be utilized in the future for manual framework control. © 2024 IEEE.

关键词： computer vision

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Segment Anything Model (SAM) to Segment lymphocyte from Blood Smear Images 3

Segment Anything Model (SAM) to Segment lymphocyte from Bloo...

引用

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

作者： Patil, Ashwini P. Hiremath, Manjunatha Department of Computer Science India Cmr Institute of Technology Department of Computer Application Bengaluru India Department of Computer Science Bengaluru India

ISBN: (纸本)9798350384598

Automated lymphocyte segmentation from smear images plays an important role in disease diagnosis and monitoring, aiding in the assessment of immune system function and pathology detection. This study proposes an approach for lymphocyte segmentation utilizing Segment Anything Model (SAM) which is a deep learning model. Our method leverages a pre trained SAM architecture and fine-tunes it on a custom dataset comprising smear images containing lymphocytes. The pretrained model's ability of versatile segmentation combined with fine-tuning on the specific dataset enhances its performance in accurately identifying lymphocyte boundaries. We evaluate the proposed approach on a diverse set of smear images, demonstrating its effectiveness in segmenting lymphocytes with impressive IOU score and Dice Score. SAM deep learning model, fine-tuned on custom datasets, holds promise for robust and efficient lymphocyte segmentation from blood smear images. © 2024 IEEE.

关键词： Image segmentation

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A Survey and Classification on Recommendation Systems 2nd

A Survey and Classification on Recommendation Systems

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2nd International Conference on Big Data, Machine Learning, and applications, BigDML 2021

作者： Sharma, Manika Mittal, Raman Bharati, Ambuj Saxena, Deepika Singh, Ashutosh Kumar Department of Computer Application National Institute of Technology Haryana Kurukshetra136119 India

ISBN: (纸本)9789819934805

In today’s modern world, the data is growing exponentially and the traditional systems are not able to fulfil the user’s requirements. To fulfil the needs of the users, various companies like Amazon, Netflix, etc. are using recommender systems which recommend content or various type of data on the basis of the user’s previous activities and interactions with the system. In the recommender system, mainly three approaches are present, i.e., content-based, collaborative filtering and knowledge-based approaches. Due to their wide applicability, recommender systems have become an area of active research and in this context, this paper furnishes a survey and comparative discussion of existing approaches. The survey draws a conclusion on how different recommendation techniques are cooperating with today’s growing technology trends and also discusses the challenges faced by them. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Collaborative filtering

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