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Pseudocode to Python - A Compiler Approach 8

Pseudocode to Python - A Compiler Approach

8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Mokshit, P. Prasad, P. Syam Reddy, N. Sumanth Belwal, Meena Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331505462

The transformation of pseudocode to Python is vital as it enables students to concentrate on the algorithms while not being distracted by the syntax and also is the key stage in software development and computer science education. This research gives a good programmer-design tool for turning pseudocode into Python,The compiler supports basic data types, control structures, loops, and functions which are the main components of the introductory programming courses. It consists of three main components: lexical analysis, syntax analysis, and code generation are the major processes of an interpreter. The implementation seeks to offer a swift and dependable way of the transformation of pseudocode to Python, thereby accelerating software development processes and improving the educational experiences. © 2024 IEEE.

关键词： Python

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Design and Development of Visual Error Detection System (VEDS) Leveraging Convolutional Neural Networks for Early Detection of Ocular Abnormalities to Mitigate Vision-Related Complications

Design and Development of Visual Error Detection System (VED...

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2024 International Conference on Computational Intelligence and computing Applications, ICCICA 2024

作者： Krishna, Kaivaram Sivarama Deepak Kumar, Bommala Reddy, Morumpalli Deekshith Saketh, Ch. Sreevidya, B. Rajesh, M. Amrita School Of Computing Department Of Computer Science And Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350306446

Ocular disorders are one of the severe eye disease The proposed system is to design and develop an automated system which captures the patient's eye image and uses the benefits of Convolutional Neural Networks (CNN) to predict whether the patient suffers from ocular eye disorders or not. The functionality integrates MongoDB database administration with a visual error detection system. The model is employed in a web-based platform that enhances the usefulness of MongoDB overall by utilizing convolutional neural networks (CNN) to interface with it smoothly. The CNN was selected based on its accuracy and trained on several datasets that included around 5000 photos for each category of eye illness from reputable databases including Eye Recognition, IDRiD, and HRF. Instant eye deployment is possible with the user-friendly website while retinal pictures load. Integration with MongoDB enhances user experience, saves and retrieves diagnostic data, and manages data better. Numerous outcomes supported the model's efficacy and indicated a significant advancement in the early diagnosis and treatment of ocular disorders. © 2024 IEEE.

关键词： Convolutional neural networks

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Compressive Sensing Based Image Compression-Encryption Scheme Using Triangular Series and Novel Chaotic Map 3

Compressive Sensing Based Image Compression-Encryption Schem...

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Sai, Kolagani Yeshwanth Ashwini, K. Karthik, Vadde Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331518592

In recent years communication through image has evolved so much that we must make sure that secret message stages should not be leaked. Hence the use of compression-encryption algorithms is of utmost necessity. In this paper, we present a unique chaotic map and encryption algorithm that can provide a robust yet efficient method for securing digital images against unauthorized access. The proposed encryption scheme begins by employing the triangular series to rearrange the pixel values of the input image. For the encryption process to offer a high level of security, a chaotic map is designed to display desirable qualities like ergodicity, unpredictability, and sensitivity to the initial key values. image compression through the Compressive sensing approach because of its lightweight implementation and suitability for resource-constrained environments like real-time image processing applications and Internet of Things devices. To prove the robustness of the chaotic map and encryption algorithm, many quantitative evaluation metrics such as entropy, correlation, bifurcation etc are used. © 2024 IEEE.

关键词： Image compression

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FER-Net: Leveraging Attention Mechanisms in EfficintNetB5 model for Enhanced Emotion Recognition in Augmented Images 5

FER-Net: Leveraging Attention Mechanisms in EfficintNetB5 mo...

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

作者： Sulochana, Balla Charishma Pragada, Bhavya Sri Reddy, Gaddam Anvith Singh, Rimjhim Padam Palaniswamy, Suja Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350376685

Facial emotion recognition is a critical component in the development of advanced human-computer interaction systems, with applications spanning security, healthcare, and social robotics. This study explores the efficacy of advanced deep learning models, specifically EfficientNetB5 model in identifying emotions from facial images. Leveraging with augmented data, our approach integrates attention mechanisms, batch normalization, convolutional layers, global average pooling layers, and dropout techniques to enhance model performance. The proposed model demonstrated significant performance accuracy of 87.5%, highlighting the importance of emphasizing relevant image regions for better emotion detection. Additionally, we conducted a comparative analysis with other prominent deep learning architectures, including AlexNet, XceptionNet, ResNet, MobileNet and EfficientNetV2M and proved the superiority of the proposed model against them. © 2024 IEEE.

关键词： Contrastive Learning

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Analysis of Green computing Models on AWS Using Machine Learning Algorithms 1

Analysis of Green Computing Models on AWS Using Machine Lear...

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1st International Conference for Women in computing, InCoWoCo 2024

作者： Reddy, T. Sudeep Beena, B.M. Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham Dept of Computer Science and Engineering India

ISBN: (纸本)9798331518943

The increasing demand for computational resources has led to a surge in energy consumption, posing significant environmental challenges. This study analyzes green computing models on Amazon Web Services (AWS) by leveraging machine learning algorithms to optimize resource utilization and minimize energy consumption. Using a dataset of virtual machine (VM) metrics-such as CPU and memory usage, network traffic, and power consumption-various machine learning models, including Random Forest, Support Vector Machines (SVM), and Neural Networks, are employed to predict energy efficiency and identify key factors influencing sustainable computing. The analysis focuses on understanding the relationship between resource utilization and power consumption and how task characteristics like type and priority impact energy efficiency. By exploring AWS services, such as EC2 and Auto Scaling, and applying machine learning techniques for real-time resource allocation, this research provides insights into reducing the carbon footprint of cloud operations. The findings contribute to the development of intelligent resource management strategies, promoting eco-friendly computing practices in cloud environments while maintaining operational efficiency. © 2024 IEEE.

关键词： Adversarial machine learning

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MVI and Forecast Precision Upgrade of Time Series Precipitation Information for Ubiquitous computing

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Informatica (Slovenia) 2023年第5期47卷 83-94页

作者： Tripathi, Ashok Kumar Gupta, P.K. Saini, Hemraj Rathee, Geetanjali Department of Computer Science and Engineering Jaypee University of Information Technology Waknaghat173234 India School of Computing DIT University Dehradun248009 India Department of Computer Science and Engineering Netaji Subhash University New Delhi India

Missing values are the major problem in a time - series dataset that led to a very typical phenomenon in a dataset analysis. Better analysis of a dataset in ubiquitous computing can only be achieved after efficiently handling the missing values. This paper envisioned to present the missing values imputation (MVI) in the monthly rainfall dataset in India. Generally, the approximation of the missing values was completed using Non-linear principal component analysis, which is having a scope of enhancement. Kalman filter using Arima model to impute missing values is a better way that can be further improved by using Extended Kalman filtering. Further, a rainfall prediction is carried out using LSTM along with three different optimizers including stochastic gradient descent (SGD), RSM-Prop, and ADAM optimizers. In addition, a comparative prediction of rainfall is depicted which shows the combination of Extended Kalman imputation, LSTM, and ADAM optimizer to outperformance in prediction. Extended Kalman Filter (EKF), which was the primarily industrialized for the Apollo Mission. The EKF is built for the predication that is the linearization of the *** this research proposes for enhanced extended kalman filter for missing values imputation. The outcomes display that the proposed EKF with the data imputation is able to precisely estimation the rain fall compactness even when data is not available, and it help for weather prediction. © 2023 Slovene Society Informatika. All rights reserved.

关键词： Time series

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Facial Grid Environment for Vitiligo Detection: A Deep Reinforcement Learning Perspective 2

Facial Grid Environment for Vitiligo Detection: A Deep Reinf...

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2nd IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

作者： Adwayan, P.C. Anfas Hassan, V. Suresh, Navneeth Nisha, K.S. Sha, Akhbar Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Amritapuri India

ISBN: (纸本)9798350360523

This research addresses the challenging task of vitiligo detection within facial images through the lens of Deep Reinforcement Learning (DRL). Leveraging the Skin Disease dataset from Kaggle, our study focuses on the integration of three distinct convolutional neural network architectures - ResNet, VGG, and EfficientNet - within the DRL framework. Facial landmarks, crucial for spatial awareness, were extracted using OpenCV as a preprocessing step. The DRL agent navigates through a grid overlaying facial images, with states corresponding to grid cells, to reach the vitiligo-affected region and maximize cumulative rewards. Analysis of the models revealed ResNet's superior stability in the loss curve, indicative of its effective feature learning. The classification accuracy further reinforced ResNet's prominence, reaching an impressive 97%, outperforming VGG (94.5%) and EfficientNet (95.3%). While VGG and EfficientNet demonstrated competitive performances, ResNet's robustness and accuracy position it as the preferred architecture for vitiligo detection in facial images. This study contributes to the burgeoning field of dermatological diagnostics by showcasing the efficacy of DRL and different CNN architectures in automating vitiligo detection. The findings not only advance our understanding of the interplay between deep learning and dermatology but also lay the foundation for more accurate and efficient models in the broader landscape of skin disorder identification. © 2024 IEEE.

关键词： Reinforcement learning

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Design of an Automatic Washing Machine Control System using Deterministic Finite Automata 5

Design of an Automatic Washing Machine Control System using ...

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

作者： Madhuja, C. Bhanot, Shreya Srinidhi, M. Radha, D. Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amrita School of Computing Bengaluru India

ISBN: (纸本)9798350376685

A Deterministic Finite Automaton (DFA) is a theoretical model of computation used to design and analyze the behavior of systems with a finite number of states, where each state transitions deterministically based on input symbols. This model ensures a clear and predictable progression from one state to another. The integration of Deterministic Finite Automata (DFA) into the control system of new-age automatic washing machines marks a significant leap forward in efficiency and user-friendliness. A logical and well-organized model for the machine's behavior by detailing every step of the washing process as separate states inside the DFA framework has been created in this project. Based on variables like stain intensity, load size, and user preference the proposed model dynamically modifies its cleaning strategy to ensure best performance while consuming the least amount of water and energy. Furthermore, by incorporating all possible states and transitions, the DFA structure allows the project to resolve possible problems and ensure continuous operation for the duration of the wash cycle. The comprehensive incorporation of all possible states and transitions within the DFA allows for resolving potential issues, ensuring uninterrupted operation throughout the wash cycle. This organized depiction of control logic improves efficiency and reliability and provides users with a clearer understanding of their washing machine's functionality, enabling seamless operation and maintenance. In conclusion, utilizing DFA in the control system of washing machines facilitates the creation of an error-free machine, enhancing both performance and user satisfaction. © 2024 IEEE.

关键词： Washing machines

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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...

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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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9th IEEE International Conference for Convergence in Technology, I2CT 2024

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

ISBN: (纸本)9798350394474

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. © 2024 IEEE.

关键词： Large datasets

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