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Enhanced Art Style Classification with VGG16 and Machine Learning Techniques 5

Enhanced Art Style Classification with VGG16 and Machine Lea...

引用

5th IEEE Global Conference for Advancement in Technology, GCAT 2024

作者： Karthik, Garikipati Muppala, Tejaswi Vinitha Chowdary, A. Pati, Peeta Basa Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350376685

Art is an expression of human creativity and culture that has greatly impacted history. Classifying art styles is an important process in art and culture to preserve cultural heritage and facilitate educational and research purposes. The proposed work presents a comprehensive study that focuses on classifying 13 different art styles using deep learning techniques. The VGG16 convolutional neural network was employed for feature extraction using its convolutional and pooling layers up to the 'fc1' layer, generating 512 and 4096-dimensional feature vectors. To evaluate the classification performance, seven machine learning classifiers were utilized. The classification accuracy is evaluated before and after applying Principal Component Analysis (PCA), to reduce the dimensionality of the feature vectors. Additionally, the paper explored the direct classification by the VGG16 model. Results prove that deep feature learning with the combination of traditional classifiers of machine learning is efficient. © 2024 IEEE.

关键词： Convolutional neural networks

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Comparative Analysis of Eminent Algorithms for Detecting Ham and Spam Contents in OSN 4

Comparative Analysis of Eminent Algorithms for Detecting Ham...

引用

4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Ponnuchamy, Kardeepa Subbulakshmi, N. School of Computing Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Virudhunagar India

ISBN: (纸本)9798331540364

Social or Communal media platforms is a lay of exchanging the thoughts or ideas of online users through various data formats like text content, image content or through audio, video, Graphics Interchange Format (GIF) etc and user can dynamically share real-time interactive events in a cost effective manner. Nowadays, with emerging fast-growing tools and technology, online socio-communal networks have shattered popularity as the central thought is to offer a framework for virtual users to link with their associative friends, subordinates and family members that blowouts their messages instantly which in turn paves a way for attackers to gain information or hack online user's data by unauthorized access. Extraction of contents is not accurate due to inflexible rule sets and some contents which are furnished in short texts are more difficult to correlate with their semantics and thereby it is difficult to prevent the display of undesired data in the existing system. Hence, the trained model system needs architecture with Enhanced Adaptive Rule-sets to overcome the inflexibility. Adaptation rule is employed by incorporating the blacklist mechanism along with eminent Machine learning algorithms to predict and prevent the undesired data. Information filtering system and Content filter removes redundant, irrelevant or unwanted information using user feedbacks with multiclass data labels. Furthermore, self-protective approaches are emphasized to achieve fidelity and ensure the trustworthiness in any micro blogging services or in online social networks. © 2024 IEEE.

关键词： Tweets

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Cancer Classification using Gene Expression Dataset to Detect Cancer 15

Cancer Classification using Gene Expression Dataset to Detec...

引用

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

作者： Nishanth, Y. Meghraj, G.K. Kuttan, Akshat Sahay, Apurvanand Bangalore Amrita Vishwa Vidyapeetham Amrita School of Computing Dept of Computer Science and Engineering India

ISBN: (纸本)9798350370249

Cancer classification plays a very important role in personalized medicine and targeted therapy. To classify the kinds of cancer based on the gene expression data, three different kinds of algorithms are applied in this paper: the greedy method, the Recursion Tree Method, and Hashing. The selection of these algorithms forms a large combination to address the complexity of the gene expression datasets in an attempt to increase the accuracy of cancer classification and minimize the computational time. The study evaluates the performance of each of the algorithms and also show how well they do in cancer classification through detailed experimentation and review. The Greedy Algorithm and Feature Hashing enable accuracy of 88% and 85%, respectively. Feature Hashing reduces the computational complexity significantly. Such findings bring out the trade-offs between accuracy and computational efficiency, giving insights that might be of importance for cancer detection and treatment methods. © 2024 IEEE.

关键词： Trees (mathematics)

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AutoGrading Classification of Tamil Descriptive Answers FastText and Roberta-Base Sentence Embedding 5

AutoGrading Classification of Tamil Descriptive Answers Fast...

引用

5th IEEE Global Conference for Advancement in Technology, GCAT 2024

作者： Elango, Vaan Amuthu Pati, Peeta Basa Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350376685

Considering the challenges associated with manually grading student answer scripts and the significance of the language, an automated approach is developed for assessing Tamil descriptive answers. Three-way assessment system of correct, incorrect, and partially correct is used to improve the autograding categorization accuracy. A total of eight datasets derived from four different dataset's sentence embedding produced by sophisticated natural language processing techniques RoBERTa-Base and FastText was created. To determine the optimal combination of data input, ten models inclusive of deep learning and machine learning models were utilized to perform a grade-based classification. This classification study proved that combination of the QuestionAnswer dataset with FastText embedding yielded the most promising result of F1 Score as 92.1%. The automatic grading methods tailored to the nuances of Tamil language are improved by this research. © 2024 IEEE.

关键词： Embeddings

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SQL Query Recommendation Based on Matrix Factorization 4th

SQL Query Recommendation Based on Matrix Factorization

引用

4th International Conference on Innovations in Computational Intelligence and computer Vision, ICICV 2024

作者： Anantatmula, Sri Harshitha Krishnan, Rohith Harikumar, Sandhya Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9789819769919

The primary objective of this study is to enhance query processing performance through the integration of sophisticated data reduction techniques within databases. Operating within the domain of in-database analytics, the focus is on evaluating efficient methods for traversing extensive data sets, expediting query processing, and deriving valuable insights using decomposition techniques, specifically QR decomposition. This study aims to achieve two primary goals: firstly, the implementation of decomposition techniques such as QR within a relational database using SQL as an operator, and secondly, the utilisation of column pivoting mechanisms on the reduced data for query recommendation. This amalgamation serves as an experimental pilot space for the innovative hypothesis of integrating query recommendation through the matrix factorization algorithm, QR factorization, using SQL. The core motivation behind the in-database approach for the query recommendation lies in the principle that analysing data where it resides is optimal for the most effective analysis. In this context, query recommendation within in-database analytics streamlines the process of data analysis and exploration by suggesting appropriate queries derived from user behaviour. These recommendations are instrumental in improving efficiency, providing relevant answers, and optimising the overall analysis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Matrix factorization

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Sudoku Solver Algorithms: A Comparative Analysis 1

Sudoku Solver Algorithms: A Comparative Analysis

引用

1st International Conference for Women in computing, InCoWoCo 2024

作者： Manyam, Yashaswini Sundaram, Srinidhi Sahay, Apurvan Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798331518943

The exploration of advanced algorithms for solving Sudoku puzzles reveals unique insights into their performance and effectiveness. The study analyzes and compares effectiveness in solving Sudoku puzzles with the classic backtracking method, an innovative approach based on ant colony optimization, and advanced constraint propagation algorithm. The project evaluates the performance of these algorithms about solution quality, computation time, and memory usage concerning the complexities of different puzzles: $9\times 9,\ 16\times 16$, and $25\times 25$ grid dimensions. Each individual of the different grid sizes tests time and space computations for each algorithm. These algorithms are putting up certain types of strengths and weaknesses in practical applications. The results, on average, produce that out of all, the ant colony optimization algorithm, which gives a good overall performance in terms of time and memory. © 2024 IEEE.

关键词： Optimization algorithms

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Image Super-Resolution by Augmentation of Region Information by Rapid Segmentation 1

引用

International Conference on Applied Soft computing and Communication Networks, ACN 2023

作者： Satish, M. Palaniswamy, Suja Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9789819720040

ISBN: (纸本)9789819720033

Spatial Super-Resolution (SR) of images finds practical applications in multiple domains like resolution enhancement of old images, improvement of resolution where imaging systems are limited, like microscopy, surveillance and remote sensing. Out of the many techniques available, deep learning-based models have shown improvement performance when augmentation methods like geometric transformations and information-based fusion augmentation techniques are used. In the current work, the technique chosen is Single Image Super-Resolution (SISR) using segmentation information augmentation, a form of fusion technique on the Caltech University Birds 200 (CUB) dataset, which contains images of birds. The deep learning model, which was used for performing SR is based on EDSR technique. It was found that the best performing hyper-parameters were 64 filters with 8 residual blocks. Also, the performance of region augmented EDSR was around 0.5 dB better. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Birds

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Comparative Study on Classification of Images in Compressed and Uncompressed Domains 9

Comparative Study on Classification of Images in Compressed ...

引用

9th International Conference on Wireless Communications, Signal Processing and Networking, WiSPNET 2024

作者： Ashwini, K. Neha, Perla Sree Jaiadityan, S. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350350845

Image Classification is the basis of computer vision. Classification with image data finds a variety of applications in various fields. A comparative study of Classifying the images in the compressed and uncompressed domain is exploited in this research. A garbage classification dataset with 6 different categories is employed for this purpose. Naitve Convolutional neural network is utilized as the classifier. Compressed classification is achieved by compressing the images using discrete wavelet transform of three levels and by feeding the compressed data to the model for classification. The accuracy achieved by classification in the uncompressed domain is about 82.1% with a loss of 0.892. The accuracy achieved in the compressed domain by the three levels of the discrete wavelet transform is 81.7%,81.2%, and 74.6% with losses of 0.793, 0.891, and 1.071 respectively. There is minimal difference in accuracy achieved between uncompressed and compressed domains. Image compression by discrete wavelet transform overperforms the uncompressed domain by several factors like size, elapsed time and memory space. © 2024 IEEE.

关键词： Discrete wavelet transforms

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AMOSI:Attention-Based Multimodal Sentiment Intensity Analysis with LSTM-CNN Architecture for Online Opinion Videos 15

AMOSI:Attention-Based Multimodal Sentiment Intensity Analysi...

引用

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

作者： Sree, V. V. V. Bhagya Bharathi Mohan, G. Prasanna Kumar, R. Rithani, M. Pallavi, Gundala Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350370249

Through online video sharing websites, people share their thoughts, experiences, and reviews on a daily basis. Researching the subjectivity and sentiment in these opinion videos is becoming more and more popular among academics and industry professionals. Although sentiment analysis works well for text, there isn't much research done on it when it comes to videos and other multimedia. The largest obstacles to research in this area are the absence of appropriate methodology, baselines, datasets,and statistical analysis of the relationships between data from various modality sources. The Multimodal Opinionlevel Sentiment Intensity dataset (MOSI), the first opinion-level annotated corpus of sentiment and subjectivity analysis in online videos, is presented to the scientific community in this paper. With labels for subjectivity, sentiment intensity, per-frame and per-opinion annotated visual features, the dataset is meticulously annotated. User-generated opinion videos are increasingly prevalent, necessitating advanced sentiment analysis techniques. We present an attention-based LSTM-2D CNN model for multimodal sentiment intensity prediction. Our contributions are three-fold: 1) We collect new annotations of visual gestures and acoustic effects in online videos to augment the MOSI dataset. 2) We design an end-to-end neural architecture to model textual, visual and acoustic modalities. Bi-directional LSTMs capture temporal dependencies in text and audio. 2D CNNs extract spatial visual features. A hierarchical attention mechanism identifies important utterances and frames. 3) Extensive experiments demonstrate 74.7% accuracy, outperforming prior BC-LSTM models by 1.7%. Ablation studies validate the impact of multimodal fusion and attention. Our work provides an improved neural baseline on MOSI, with detailed analysis of multi-view sequential modelling for opinion videos. The additional annotations and strong results will aid future research in this emerging domain. © 2024

关键词： AMOSI Attention CNN LSTM MultiModal

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Airfoil Self Noise Prediction Using Machine Learning and Explainable AI 6

Airfoil Self Noise Prediction Using Machine Learning and Exp...

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6th IEEE International Conference on Recent Advances in Intelligent Computational Systems, RAICS 2024

作者： Pandey, Honey Pandey, Prateek Gupta, Deepa Amrita School of Computing Department Of Computer Science And Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350381689

A major environmental challenge in the aerospace sector is presented by aircraft noise, particularly airfoil noise. Traditional methods for predicting airfoil noise involve complex and computationally intensive numerical simulations. This research explores the use of machine learning to enhance the efficiency and accuracy of airfoil noise prediction. By utilizing extensive datasets from wind tunnel experiments, encompassing a variety of airfoil geometries and flow conditions, supervised learning algorithms are employed to construct a predictive model. Diverse regression models, including random forests, support vector regression, CatBoost, XgBoost, Gradient Boosting and decision trees, are trained on the NACA 0012 airfoil dataset to uncover correlations between airfoil characteristics and noise emissions. Initial findings underscore the potential of machine learning, with Gradient Boosting regression achieving a noteworthy R2 score of 0.96625 and RMSE as 1.2622. This study not only equips engineers with a valuable tool for optimizing airfoil shapes to reduce noise but also establishes the groundwork for real-time noise prediction systems in aircraft operations. © 2024 IEEE.

关键词： Random forests

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