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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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Visual Question Answering in Malayalam Text 3

Visual Question Answering in Malayalam Text

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3rd International Conference on Sentiment Analysis and Deep Learning, ICSADL 2024

作者： Joseph, Jose Ram, V Anand YadhuKrishna, P. Anjali, T. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amritapuri India

ISBN: (纸本)9798350396157

One of the most important tasks in combining computer vision and natural language understanding is Visual Question Answering (VQA). This paper focuses on the VQA problem in Malayalam, a language known for its unique Dravidian linguistic characteristics. The robust LXMERT model, a bidirectional encoder-decoder architecture capable of handling multimodal input, is used to achieve this goal. Extensive testing on an independent set of Malayalam images and questions highlights the feasibility of building a trustworthy VQA model on a Malayalam dataset. The method shows adaptability when answering questions about Malayalam images, which creates opportunities for image retrieval, annotation, and possibly image-based machine translation. This demonstrates how the model can be applied to real-world situations in a variety of fields. © 2024 IEEE.

关键词： computer vision

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Detecting Malicious Threats On Mobile Applications Using Machine Learning Approaches 3

Detecting Malicious Threats On Mobile Applications Using Mac...

引用

3rd International Conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Padmavathi, A. Yasaswy, K. Rahul, K.Jayesh Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science Engineering Chennai India

ISBN: (纸本)9798331528201

In Today scenario , Android is one of the most frequently used mobile operatings systems, thus it is a priority for advanced threat actors and hackers. Malicious code is often found in Android applications to which security solutions have a problem of identifying the threats. Today's Android malware is sophisticated enough that it can avoid most ***, machine learning is more efficient in stopping a large number of diverse contemporary threats to Android devices. These machine learning methods essentially first rely on the detection of sequential patterns of malicious behaviors, and secondly, distinguish between existing and new types of threats that have previously exhibited typical and atypical behavior. Our proposed approach builds on Ensemble learning where the methods include Support Vector Machines, Decision Trees, Logistic regression and XGBoost to bolster the detection abilities of the model. This methodology serves to enhance the specific model performance on identifying known malware. © 2024 IEEE.

关键词： Adversarial machine learning

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Tri-Model Synergy: Redefining Depression Detection through the Convergence of ANN, CNN, and SVM 5

Tri-Model Synergy: Redefining Depression Detection through t...

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： George, Angelina Baniya, Achal Jose, Alphonsa Kumar, K. Dinesh Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798331504403

Depression being a universal mental health challenge that affects millions globally, necessitating reliable detection methods in order to aid in timely intervention. Like physical health, mental health is not to be ignored. This study presents a novel hybrid model that combines Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Support Vector Machines (SVM) to analyze and detect depression in individuals. The hybrid model achieved a training accuracy of 95.6% and a test accuracy of 91.2%, demonstrating its robustness and reliability. The total training time was recorded at 3.01 seconds, with ANN contributing 1.11 seconds and CNN contributing 1.9 seconds. Furthermore, the model's performance metrics included an F1-Score of 0.91 and 0.91 for detecting the classes of absence and presence of depression respectively, highlighting its effectiveness in correctly identifying depressive states. This hybrid approach provides a comprehensive analysis by leveraging the strengths of each individual model, paving the way for more reliable and scalable mental health assessment tools. © 2024 IEEE.

关键词： Convolutional neural networks

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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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Deep Learning based Terrain Classification of Mars Raw Images using UNet and FCN Models 18

Deep Learning based Terrain Classification of Mars Raw Image...

引用

18th INDIAcom;11th International Conference on computing for Sustainable Global Development, INDIACom 2024

作者： Samhitha, D. Sai Ashwini, K. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science Engineering Chennai India

ISBN: (纸本)9789380544519

The exploration of Mars has consistently been of utmost significance in understanding planetary development, suitability for life, and the possibility of establishing civilizations. Precise classification of Martian terrain is essential for effective navigation, identification of resources, and prevention of potential dangers, to enhance the effectiveness of Mars missions. Traditional methods of terrain categorization frequently depend on manual examination and have constraints in terms of scalability and precision. This paper introduces an innovative method that utilizes deep learning algorithms to completely transform the process of classifying Martian terrain. This study employed the Unet and FCN models to categorize the Martian surface by utilizing a wide variety of images obtained from the AI4MARS dataset, from which a collection of 5K to 15K images are extracted. The results show the effective performance, the FCN model attained an Intersection over Union (IoU) score of 73%, while the Unet model acquired a score of 60%. The results emphasize the capacity of deep learning to revolutionize the classification of the Martian landscape, providing a more precise and adaptable answer in contrast with traditional approaches. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： AI4MARS Deep Learning Fully Convolution Network (FCN) Mars Exploration ResNet50 Terrain Recognition UNet

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A Machine Learning Based Auto-Grading Model for Question-Based Algebra Problems using RoBERTa 4

A Machine Learning Based Auto-Grading Model for Question-Bas...

引用

4th International Conference on Intelligent Technologies, CONIT 2024

作者： Vidula, N.A. Reddy, T. Anisha Manogna, Sodum Balakrishnan, Roshni M Pati, Peeta Basa Amrita School of Computing Dept of Computer Science and Engineering Amrita Vishwa Vidhyapeetham Bengaluru India

ISBN: (纸本)9798350349900

The use of Machine learning models into educational assessment has revolutionized the grading process for problemsolving *** research introduces a machine learning auto-grading model tailored for question-based algebra problems, utilizing RoBERTa, a natural language processing *** primary objective is to investigate the adaptability of RoBERTa, originally designed for natural language understanding, for representing non-natural language mathematical expressions. The study also explores the impact of different embedding strategies on the performance of autograding systems for student responses to equation *** mathematical and textual code inputs were transformed into vectors, and the vectors were then fed into several models. Evaluation metrics such as RMSE, MSE, R2, and MAE are utilized to assess the effectiveness of each embedding approach in grading student responses accurately. The use of k-fold cross-validation will improve the dependability of the assessment measures, offering insights into the model's consistency across different subsets of the data. The comparative analysis of different embedding strategies offers guidance on selecting the most suitable approach for enhancing the autograding process. © 2024 IEEE.

关键词： Embeddings

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Amyotrophic lateral sclerosis Disease Detection Through Facial Expression Analysis

Amyotrophic lateral sclerosis Disease Detection Through Faci...

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2025 International Conference on Ambient Intelligence in Health Care, ICAIHC 2025

作者： Sri Varshan, P. Harigaran, R. Vamsi Krishna, V. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai campus India

ISBN: (数字)9798331530259

ISBN: (纸本)9798331530259

Identifying Amyotrophic Lateral Sclerosis (ALS) early is crucial for initiating treatment, improving prognosis, and enhancing the quality of life for affected individuals. However, early diagnosis and symptom detection can be challenging. A more accessible and cost-effective solution involves computational analysis of the patient's facial expressions. ALS patients exhibit distinct facial muscle movements during specific actions, such as mouth opening, compared to healthy individuals. This paper proposes using deep learning-based facial expression analysis to detect ALS. The model was trained to recognize ALS symptoms through facial expression data and evaluated on the NeuroFace dataset. Experimental results show that the model achieved a high precision of 0.94, a recall of 0.89, and an F1-score of 0.92 for the ALS class. The overall accuracy of the model was 92%, demonstrating its effectiveness in distinguishing between ALS and healthy individuals, thus contributing to early ALS detection through non-invasive methods. © 2025 IEEE.

关键词： Noninvasive medical procedures

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Exploring the Landscape of Lung Diseases: A Comprehensive Literature Survey 8

Exploring the Landscape of Lung Diseases: A Comprehensive Li...

引用

8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Soumya, E. Santhanalakshmi, S. Amrita School of Computing Department of Computer Science & Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350367904

The lung is the sensitive organ of our body. Modern developments in deep learning and machine learning have completely changed how medical imaging is used to diagnose and classify lung problems. Consequently, there is a wealth of literature exploring the application of these technologies in this domain. This study undertakes a broad survey of the utilization of deep learning and machine learning methods for diagnosing lung illnesses in medical images. Its primary objectives include delineating the taxonomy of cutting-edge deep learning-driven systems for detecting lung diseases, interpreting recent research trends, and identifying persistent challenges and potential future directions. By proposing a machine learning-based classification of contemporary lung diseases, this research aims to furnish a framework crucial for researchers across various domains to structure their contributions and investigations. This classification encompasses several common survey attributes, including the types of data utilized, the spectrum of lung diseases addressed, and the collection of machine learning and deep learning procedures employed. Such a framework is pivotal for augmenting the efficacy and accuracy of machine learning in the precise identification and categorization of lung disorders, thereby minimizing diagnostic errors. © 2024 IEEE.

关键词： Lung cancer

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Emotion and Cause Detection in Dialogues Using Capsule Networks 1

Emotion and Cause Detection in Dialogues Using Capsule Netwo...

引用

1st International Conference for Women in computing, InCoWoCo 2024

作者： Ganesh, Shyam K. Lavanya, G. Priya, S Baghavathi Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331518943

Identifying the sources of emotions expressed in text-to-text conversational dialogs significantly contributes to improving applications such as virtual assistants, customer support, and interventions aimed at improving mental well-being. Traditional approaches to emotion analysis are primarily focused on classification procedures rather than consideration of the underlying causes responsible for triggering specific emotional behaviors in various contexts. This paper has presented a new model that identifies emotions in conversational data but simultaneously enables the determination of the root causes of such emotions. The framework uses DistilBERT to generate subtle contextual embeddings, which incorporate the richness of meaning for each utterance. For that purpose, it uses a Capsule Network for the efficient modeling of sequential dependencies and hierarchical relationships that exist in dialog. This altogether makes this system decode more complex emotional triggers with greater accuracy. Furthermore, the model is designed to be scalable and efficient, meaning it can comfortably be deployed in any real-time environment with minimal computational resources. The Experimental Results, integrated A two-step model consisting of Distil BERT and Capsule Network greatly enhances the accuracy of emotion cause detection and can be a strong solution for applications requiring minute emotions and causal inferences from conversational data. © 2024 IEEE.

关键词： Emotion Recognition

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Advanced Hieroglyphic Braille Transpixelation (AHBT): A Novel Approach to Secure Image Steganography 5

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

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

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

ISBN: (纸本)9798350376685

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. © 2024 IEEE.

关键词： Pixels

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