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检索条件"机构=MTech Computer Science and Engineering School of Computing Science and Engineering"

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Unveiling Delta Airline Experiences through Topic Modeling and Traveler Persona Classification

Unveiling Delta Airline Experiences through Topic Modeling a...

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Global Conference for Advancement in Technology (GCAT)

作者： Drishya Baburaj Prithvi Syam D S Deepa Gupta Aarathi Rajagopalan Nair Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798350376685

ISBN: (纸本)9798350376692

Understanding customer feedback is crucial for enhancing service quality in the competitive airline industry. This study analyzes Delta Airline's customer reviews using advanced natural language processing techniques, focusing on topic modeling and traveler persona classification. Initially, the study was performed an elbow plot analysis to determine the optimal number of clusters, identifying three distinct clusters. Clustering algorithm K-means and Latent Dirichlet Allocation were then applied to uncover the main topics in the reviews. Next, this research classifies reviewers into different traveler personas using NLP models such as BERT, Word2Vec, One-Hot Encoding, DistilBERT and RoBERTa, and models which uses deep learning architectures such as LSTM, BiLSTM and GRU identifying categories like Family leisure, solo leisure, Couple leisure and business travelers. The insights from this analysis enable Delta Airline to tailor their services to specific customer segments. Classification results indicate that BiLSTM and GRU models performed the best, with BiLSTM achieving an accuracy of 92% with Roberta.

关键词： Analytical models Accuracy Reviews Atmospheric modeling Bidirectional long short term memory Encoding Data models Elbow Airline industry Business

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Enhancing Hate Speech Detection in Tamil Code-Mix Content: A Deep Learning Approach with Multilingual Embeddings

Enhancing Hate Speech Detection in Tamil Code-Mix Content: A...

引用

Global Conference for Advancement in Technology (GCAT)

作者： Vishak Anand S Ishwar Prathap Deepa Gupta Aarathi Rajagopalan Nair Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798350376685

ISBN: (纸本)9798350376692

The increasing amount of hate speech and language which are inappropriate on platforms of social media has emerged as a serious issue, prompting measures to counter it. This study examines hate speech and languages that are objectionable in a Tamil Code-Mix dataset, where Tamil content is provided in English. It assesses the performance of various deep learning classifiers that use different multilingual transformer embedding models, which includes many models such as DistilBERT, IndicBERT, XLM-RoBERTa, LASER, MuRIL, and BERT, in this domain. Among the models tested, an ensemble stacked model integrating Long Short-Term Memory and Gated Recurrent Units with XLM-RoBERTa embeddings had the greatest accuracy (0.76) and weighted F1-score (0.72). This exceptional performance underscores the potential of advanced ensemble models for distinguishing hate speech in code-mixed languages. The study's findings are notable for several reasons. They enhance understanding of hate speech in Tamil, particularly in its code-mixed variant, and offer insights into the application of advanced deep learning methods and multilingual embeddings for detecting harmful content. By improving accuracy and reliability in hate speech detection, this research contributes to fostering safer and more inclusive online environments for Tamil-speaking communities. In summary, the research advances the domain of hate speech identification and emphasizes the importance of technological solutions in creating more safer and very inclusive online spaces.

关键词： Deep learning Accuracy Social networking (online) Hate speech Stacking Semantics Transformers Data models Multilingual Long short term memory

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Optimizing Head and Neck Tumour Segmentation Using Evolutionary Algorithms and Deep Learning

Optimizing Head and Neck Tumour Segmentation Using Evolution...

引用

Advancement in Renewable Energy and Intelligent Systems (AREIS), International Conference on

作者： Abin Joseph Dhanya M Dhanalakshmy Department of Computer Science and Engineering Amrita School of Computing Coimbatore Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798350387230

ISBN: (纸本)9798350387247

The paper explores the use of deep learning models for accurate head and neck tumor segmentation, aiming to improve efficiency and accuracy in medical imaging. The main focus is to employ evolutionary algorithms to optimize these models, minimizing hyper parameters for better results. Traditional manual segmentation is labor-intensive and prone to variability; thus, this study automates segmentation using models like U-Net, V-Net, and SegNet, enhancing patient care through accurate early diagnosis. Key metrics such as Dice Coefficient, Structural Similarity Index, and Intersection over Union are used for performance evaluation, while MRI data from a dataset are preprocessed for model training. The proposed optimized U-Net architecture demonstrates promising results, suggesting that evolutionary algorithms can enhance deep learning models for medical image analysis.

关键词： Deep learning Image segmentation Analytical models Accuracy Head Evolutionary computation Data models Neck Tumors Genetic algorithms

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Neural Machine Translation Using a Pivot Approach: Dogri to English

Neural Machine Translation Using a Pivot Approach: Dogri to ...

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International Conference on computing and Networking Technology (ICCNT)

作者： Aarathi Rajagopalan Nair Shailashree K. Sheshadri A. Dhanush N.V.S Pradyumna Deepa Gupta Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The progress of Neural Machine Translation for Indic languages is gradually achieving a noteworthy result in terms of translation accuracy. However, its advancement is impeded by the lack of rich parallel corpora, a limitation not shared by non-Indic languages. As a result, most languages of India are still categorized as low-resource or zero-resource languages. Various state-of-the-art architectures have been developed to address these challenges, with the predominant approach being the pivot-based technique. This technique, rooted in transfer learning, utilizes an auxiliary language as a pivot to assist in translation. In this study, a pivotbased Neural Machine Translation (NMT) architecture is designed for translating Dogri to English, with Hindi as the pivot. Dogri, being a low-resource language, shares linguistic similarities with Hindi. Furthermore, Hindi-to-English state-of-the-art models are accessible, ensuring high translation accuracy. Through the implementation of this pivot-based translation model, a BLEU score of 20.97, SacreBLEU of 21.54 and chrF of 50.01 is attained.

关键词： Accuracy Computational modeling Transfer learning Neural networks Neural machine translation computer architecture Linguistics

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Machine Learning Strategies for Telecom Churn Prediction

Machine Learning Strategies for Telecom Churn Prediction

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International Conference on computing and Networking Technology (ICCNT)

作者： D Poojitha Tanmay Darak Ushnish Samaddar Manju Venugopal Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The ability to predict customer churn represents a critical component in the telecom industry’s quest for enhanced customer retention and long-term viability. In this research, several standalone machine learning algorithms, including gradient boosting and decision trees, have been applied to a dataset comprised of customer demographics and service usage patterns. The application of rigorous hyperparameter tuning and cross-validation processes has significantly improved the performance metrics, such as accuracy, precision, recall, and $F 1$ score, of these models. Furthermore, the study incorporates two advanced ensemble techniques to refine the prediction accuracy. The combination of Random Forest and Gradient Boost achieved an accuracy of 85.7%, while the integration of AdaBoost and CatBoost resulted in an accuracy of 87.3%. This approach provides telecom operators with a potent tool to proactively identify and engage customers at risk of churn, thus enabling timely and targeted retention strategies that enhance customer satisfaction and contribute to the sustainability of telecom enterprises.

关键词： Accuracy Machine learning algorithms Customer satisfaction Forestry Prediction algorithms Telecommunications Decision trees Sustainable development Tuning Random forests

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Swarm-Optimized Deep Convolutional Networks for Precise Detection and Grading of Diabetic Retinopathy

Swarm-Optimized Deep Convolutional Networks for Precise Dete...

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Advancement in Electronics & Communication engineering (AECE), International Conference on

作者： U Sadhana Tripty Singh Beena. B. M Dept. of Computer Science & Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru

ISBN: (数字)9798350364729

ISBN: (纸本)9798350364736

Diabetic Retinopathy (DR) is a very critical problem of diabetes that can lead to blindness if not detected and treated in the early stage. This research was conducted on this difficult disease based on image processing and Machine intelligence. In this research, our model detects and classifies the DR using the swarm-optimized deep convolutional networks. The proposed model integrated the convolutional neural networks (CNNs) along with Particle Swarm Optimization (PSO) to enrich the performance of the model execution and results with fine-tuned hyperparameters. This custom-optimized CNN model, designed for image classification, was evaluated on the Kaggle diabetic retinopathy dataset. The swarm-optimized deep CNN model presented balanced performance across all DR classes and achieved a precision of 0.93, recall of 0.97, and F1-score of 0.95 for all classes. The model's overall accuracy reached 0.84 on the selected dataset.

关键词： Diabetic retinopathy Accuracy Machine learning Retina Robustness Real-time systems Convolutional neural networks Particle swarm optimization Machine intelligence Image classification

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Optimising Energy Efficient Routing protocols in Wireless Sensor Networks - A comparative study

Optimising Energy Efficient Routing protocols in Wireless Se...

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Self Sustainable Artificial Intelligence Systems (ICSSAS), International Conference on

作者： Thotapalli Sri Surya Manideep Gamidi Rohan Sneha Saragadam Apurvanand Sahay Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Amrita School of Computing Bengaluru India

ISBN: (数字)9798350368413

ISBN: (纸本)9798350368420

This study presents a comparative overview of optimization techniques that include, among others, Ant Colony Optimization, Firefly Algorithm, Bat Algorithm, Bee Colony Optimization, Q-learning, Dynamic Programming, and Branch and Bound Algorithm. Addressing each domain in detail, explanations will be offered for the methods described: for example, ACO’s foraging behavior, Firefly’s light attraction, Bat’s echolocation, and BCO’s collaborative approach. The Travelling Salesman Problem (TSP) is treated with the DP algorithm and Branch and Bound. Different tests in each algorithm are made, and their efficiency and weaknesses are approximated to guide the selection of a proper approach to be used in future optimizations. This research highlights the strengths and drawbacks of each method, thus enhancing the understanding of how these optimization techniques operate in various fields and contributing to the informed selection of algorithms for specific problems. The results provide practical insights and suggest directions for further improvement and application in real-world scenarios.

关键词： Ant colony optimization Wireless sensor networks Q-learning Heuristic algorithms Approximation algorithms Search problems Routing protocols Dynamic programming Optimization Tuning

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Performance Evaluation of Data Migration Tools: A Comparative Study Based on Data Type, Size and Network Bandwidth 23

Performance Evaluation of Data Migration Tools: A Comparativ...

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Proceedings of the 5th International Conference on Information Management & Machine Intelligence

作者： Suchit Anandrao Sapate Ganesh Rajaram Pathak Omprakash Tembhurne Computer Science and Engineering MITADT University India MIT School of Computing MITADT University India

ISBN: (纸本)9798400709418

Modern organizations require the ability to seamlessly transfer data between their various environments and systems. Due to the complexity of data migration, it is important that the tools used to perform this process are evaluated. This paper aims to provide a comprehensive analysis of the performance of the different tools by taking into account network bandwidth, data type, and size. The study evaluates the performance of various data migration tools, such as Amazon Web Services' Data Migration Service (AWS DMS), Azure Data Factory, Talend's Data Integration, and Google Cloud's Transfer Service. The evaluation was performed using the Apache JMeter open-source platform. JMeter is a flexible environment that can be used for testing and evaluation. The goal of the evaluation is to replicate the real-world processes involved in data migration. The various tools' performance is evaluated by testing different scenarios that involve the extraction, transformation and loading of data. Besides data types, other factors such as network bandwidth efficiency are also taken into consideration to see how they perform in different environments. The evaluated tools' key performance metrics, such as the speed of data transfer, scalability, and error rate, are compared and analyzed. The findings of the evaluation help organizations identify the strengths and weaknesses in their data migration tools and which one is ideal for their requirements. The study's findings provide valuable information on the different tools for data migration. The evaluation process, which was carried out through the open-source Apache JMeter platform, can be used to analyze and extend the capabilities of other migration tools. The results of this evaluation can help organizations identify the ideal tool for their specific requirements and improve their performance.

关键词： Apache

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International Conference for Convergence of Technology (I2CT)

作者： Akhilesh P Amal Krishna K S Karthick Bharadwaj Manju Venugopalan Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798350394474

ISBN: (纸本)9798350394481

Technology is expanding and applicants are making their resumes look better by developing their skill set for the current environment. Each resume now looks very promising and manually screening resumes and finding out the potential candidate in time consuming and is not an efficient method. In this paper, we propose a method using Natural Language Processing (NLP) and Spark to filter relevant resumes. The work proposes a cosine similarity-based approach to measure text-similarity and hence find worthy candidates based on key information provided by the employer. The implementation of the proposed approach on a Spark platform helps in handling large-scale datasets, enabling efficient semantic similarity analysis in the context of job descriptions. The experiments conclude that the proposed method can efficiently speed up the process in short - listing worthy candidates and can effectively replace the process of hiring managers going through each resume. Our model demonstrates superior performance compared to a non-Spark-based resume filtering system, achieving an average reduction in processing time by 80%, translating to an average runtime decrease from 5 seconds to 1. This efficiency gain is scalable, presenting enhanced performance in candidate shortlisting, especially with larger datasets.

关键词： Runtime Resumes Semantics Information filters Natural language processing Sparks Convergence

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Advanced Hieroglyphic Braille Transpixelation (AHBT): A Novel Approach to Secure Image Steganography

Advanced Hieroglyphic Braille Transpixelation (AHBT): A Nove...

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Global Conference for Advancement in Technology (GCAT)

作者： Aayushi Jeeban Tripathy Siddhant Ashwani Subhiksha Suresh Rao Sahil Mishra Shinu M. Rajagopal C. R. Kavitha Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798350376685

ISBN: (纸本)9798350376692

Modern correspondence protection is highly in demand for the protection of information exchange, mainly by means of cryptography and steganography. In this paper, a new method called Advanced Hieroglyphic Braille Transpixelation, or AHBT, is proposed and introduced. AHBT amalgamates Braille and hieroglyphs into a very complex algorithm of information hiding in digital images. The plaintext message will first be translated into hieroglyphics and Braille, then encrypted into 8-bit and 6-bit binary formats, correspondingly. These are camouflaged with columnar transposition and hidden inside the 2 LSB of pixel components (r, g, b) using LSB steganography, storing 6 bits of data per pixel. Experimental results have proven AHBT to improve in data security and visual quality compared to conventional methods, evidenced through higher PSNR rates. This technique is a revolution in personal information protection, as it has used multilevel encryption, steganography, and ancient scripts in combination for better security and privacy.

关键词： Steganography Braille Visualization Privacy Image coding Digital images Encryption Protection Information exchange

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