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Automated Short Answer Grading With Word Embedding-Based Semantic Similarity Using PySpark

Automated Short Answer Grading With Word Embedding-Based Sem...

引用

IEEE Recent Advances in Intelligent Computational Systems (RAICS)

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

ISBN: (数字)9798350381689

ISBN: (纸本)9798350381696

Automated short answer grading, a pivotal advancement in educational assessment methodologies, addresses scalability challenges and streamlines evaluation processes using natural language processing and machine learning. This technology ensures timely, consistent, and objective feedback, replacing manual grading to mitigate biases and variations, thus enhancing the educational experience. The proposed model employs Word2Vec to generate word embeddings for each student response and the corresponding reference answer. By leveraging cosine similarity, the model calculates semantic similarity between the vectors, producing a score ranging from 0 to 1. This cosine similarity score is then scalable, allowing for the computation of final grades based on question-specific weightage, providing a robust and scalable approach for automated short-answer grading. In the context of this work, regression-based evaluation metrics yielded a Mean Squared Error (MSE) of 0.2727 & R 2 score of 0.67, demonstrating the efficacy of the automated grading system in achieving accurate and reliable assessments.

关键词： Accuracy Computational modeling Scalability Semantics Manuals Machine learning Natural language processing

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Shot-Level Semantic Reward-based Self-Attentional Network for Unsupervised Video Summarization 5

Shot-Level Semantic Reward-based Self-Attentional Network fo...

引用

5th IEEE International Conference on Advances in Electronics, Computers and Communications, ICAECC 2023

作者： Harish, B. Natarajan, B. Elakkiya, R. Annamalai, R. Bhuvaneswari, R. Deepak, V. Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Computer Science and Engineering BITS Pilani Dubai Campus Dubai345055 United Arab Emirates Computer Science and Engineering Koneru Lakshmaiah Education Foundation AndhraPradesh Vaddeswaram India

ISBN: (数字)9798350305449

ISBN: (纸本)9798350305449

Video summarization is the task of condensing long videos into shorter versions while preserving the important content. This research introduces a novel self-attention-based technique for unsupervised video summarizing. To produce more realistic video descriptions, the suggested method combines deep reinforcement learning with a semantic reward function at the shot level. Minimizing the impact of user subjectivity is a significant challenge in video summarization, and the reward function is designed to achieve this objective. For recording videos, shot-level semantics are employed as rules that apply to all viewers. Our approach is based on deep reinforcement learning and differs from previous methods in this area with Convolutional Neural Network (CNN) in the encoding part extracting high-level features from the input video frames and a Gated Recurrent Unit (GRU) model in the decoding part, generating the summary by decoding the features extracted by the encoder is trained on a dataset of videos and their corresponding summaries. To get a key shot summary with no need for manual annotations and learn from the input video, the suggested model combines a bi-directional recurrent neural network, reinforcement learning, and self-attention methods. The approach outperforms video summarizing methods without supervision on the SumMe and TVSum datasets. © 2023 IEEE.

关键词： Semantics

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Evaluating Performance and Accuracy of Large Language Models in Translating Code-Mixed Hindi to English: A Comparative Study

Evaluating Performance and Accuracy of Large Language Models...

引用

IEEE India Conference (INDICON)

作者： Aarathi Rajagopalan Nair Deepa Gupta Department of Computer Science and Engineering Amrita School of Computing Bengaluru India

ISBN: (数字)9798350391282

ISBN: (纸本)9798350391299

Translating code-mixed Indian languages, where more than two languages are blended in a single sentence, poses significant difficulties and challenges in translation systems. Given the wide variety of languages in India, evaluating the performance of large language models on these tasks has become increasingly important. This study conducts a comparative analysis of advanced LLMs, including BLOOMZ, GEMMA, GPT, and Gemini, focusing on their ability to translate Hindi-English text which is code-mixed. The models are evaluated and assessed based on translation accuracy and contextual consistency. Among the models evaluated, Gemini achieved a BLEU score of 20.82, demonstrating superior performance compared to the others. The analysis reveals the strengths and weaknesses of each model, highlighting the ongoing challenges in translating code-mixed content. These findings provide useful insights for model selection in real-world applications and highlight areas for future research to improve translation quality in code-mixed scenarios. As the prevalence of code-mixing continues to rise, improving the capabilities of LLMs in handling such complexities is essential for better communication and understanding in multilingual societies.

关键词： Analytical models Sentiment analysis Translation Accuracy Large language models Focusing Data models Multilingual Prompt engineering Reliability

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Neural Networks and Evolutionary Algorithms Based Approaches for engineering Recommendation Systems – A Comprehensive Review

Neural Networks and Evolutionary Algorithms Based Approaches...

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Emerging Trends in Information Technology and engineering (ic-ETITE), Conference on

作者： Priyadharshini B N Apoorva Arvapalli Alekhya Gajula Sujan Sagar Jeyakumar G Department of Computer Science and Engineering Amrita School of Computing Coimbatore India

Recommendation systems are the subset of data filtering techniques and focus on providing personalized suggestions to the users. The systems rely on the data to provide insightful suggestions. Over the years, recommendation system has gained huge prominence in boosting e-commerce platforms, and online personalized services to users. Along with their popularity, these systems also face challenges such as data sparsity, time constraints, and scalability. As a result, various approaches are opted along with the traditional techniques to optimize the process. This paper reviews the various approaches followed in engineering the recommendation systems and analyzes their efficiencies. The approaches focus primarily on understanding the involvement of neural networks and evolutionary algorithms and their performance compared to other systems involved with approaches like clustering techniques, linear-based techniques, probabilistic models, etc. This paper also provides insights into the various hybrid approaches followed by the different recommender systems involving neural networks and evolutionary algorithms.

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AI-Driven Stress Analysis and Management: A Novel Approach Leveraging OpenAI and NoSQL Databases to Empower Students in Stress Management and Promote Overall Student Well-being and Academic Progression

AI-Driven Stress Analysis and Management: A Novel Approach L...

引用

Computational Intelligence and computing Applications (ICCICA), International Conference on

作者： B. Akhileswar Reddy G. Someswara Reddy K. Lokesh Sreevidya B Rajesh M Department of Computer Science and Engineering Amrita School of Computing Bengaluru India

ISBN: (数字)9798350306446

ISBN: (纸本)9798350306453

Now a days, students are under a lot of stress due to social issues and schoolwork. Additionally, they use smart devices excessively, which negatively impacts their mental health. an online application that allows users to actively interact with the system to get recommendations and insights specific to their level of stress. This ground-breaking fusion establishes the foundation for an approachable and proactive stress-reduction solution. This project aims to produce a novel stress analysis tool by integrating NoSQL databases with OpenAI's superior natural language processing, specifically the GPT-3.5 model. Real-time stress analysis is made possible by the system's ability to collect extensive stress-related data from users by utilizing OpenAI's superior language understanding skills. Scalability is enhanced by the incorporation of NoSQL databases, which guarantee effective unstructured data retrieval and storage.

关键词： Analytical models Databases NoSQL databases Scalability Human factors Mental health Real-time systems

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Multi-Criteria Adaptive Websites using Grey Wolf Optimization

Multi-Criteria Adaptive Websites using Grey Wolf Optimizatio...

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Computational Intelligence for Green and Sustainable Technologies (ICCIGST), International Conference on

作者： Santosh Kumar Jyoti Bisht Tarun Sarawgi School of Computing Science and Engineering Galgotias University Greater Noida India

ISBN: (数字)9798350381887

ISBN: (纸本)9798350381894

The World Wide Web is an international network of linked files and resources that may be viewed online. It consists of web pages, multimedia files, and apps connected by hyperlinks for a variety of uses, including e-commerce and communication. This paper explores the diminishing efficacy of conventional SEO techniques such as PageRank and HITS Algorithm in the rapidly evolving online environment. To address this challenge, the research investigates the application of natural algorithms, including Grey Wolf Optimization (GWO), Ant Colony Optimization (ACO), and the Best-Fit Search Algorithm, to optimize web page ranking. The study focuses on the utilization of these adaptable algorithms to adaptively rearrange website content, enhancing user engagement and navigation. Experimentally it has been shown that the proposed GWO based algorithm for selecting top ranked web links achieves $0.98 \%$ of improvement in cost keeping runtime comparable with other algorithms.

关键词： Ant colony optimization Runtime Navigation Web pages Web mining Search engines Information management Web sites Multimedia communication Optimization

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Explainable AI-based Detection and Interpretation of Abnormalities in Chest X-rays

Explainable AI-based Detection and Interpretation of Abnorma...

引用

International Symposium on Advanced computing and Communication (ISACC)

作者： Sai Smrithi Muthukumar Bhavika Gandham Trisha Vijayekkumaran Jyotsna C Aiswariya Milan K Dept. of Computer Science and Engineering Amrita School of Computing Bengaluru India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Chest X-ray images are widely used in diagnosing medical conditions, however, due to radiologist fatigue and shortage of resources the possibility of spotting peculiarities increases. This work proposes an explainable AI system to identify, quantify, and explain the pathology found in chest x-ray images. It builds credibility, enhances medical judgment, and reflects efficient utilization of radiology assets. This study used ResNet for the chest X-ray image classification and compared it with other related models like EfficientNetB0, DenseNet, VGGNet, and MobileNet. ResNet had the best AUC value of 0.826 thereby garnering it the best classification accuracy hence validating its selection as the main model. Moreover, ResNet adapted explainability to provide visual and textual interpretations and to classify diseases where such actionable insights could improve the reliability of diagnosis and patient outcomes.

关键词： Measurement Adaptation models Visualization Accuracy Explainable AI X-rays Reliability X-ray imaging Medical diagnostic imaging Image classification

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Pseudocode to Python - A Compiler Approach

Pseudocode to Python - A Compiler Approach

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International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS)

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

ISBN: (数字)9798331505462

ISBN: (纸本)9798331505479

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.

关键词： Codes Translation Program processors Software algorithms Syntactics Turning Matrix converters Programming profession Python Software development management

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Intrusion Detection based on clustering with ML algorithm and LIME Insights

Intrusion Detection based on clustering with ML algorithm an...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Achal Baniya Abhay Vinod Hitesh Surya Chinta Putluru Vishnu Thangam S Department of Computer Science and Engineering Amrita School of Computing Bengaluru India

The threat landscape for cybersecurity is becoming more complex as digital technologies become increasingly sophisticated. Intrusion detection systems (IDS) are required for maintaining the integrity of a network by detecting and neutralizing any attacks. This study investigates how well machine learning (ML) approaches work to strengthen intrusion detection systems (IDS) in the face of changing cyber threats. Since traditional approaches frequently can't keep up with changing attack patterns, ML must be used to improve predictive skills. The IDS purposed clustering technique to identify anomaly behavior within particular groups by classifying network traffic into various patterns. In order to predict anomaly detection, the study investigates the application of various machine learning techniques, such as Random Forest (RF), Adaptive Boosting (AdaBoost), Decision Tree (DT), K-nearest Neighbors (KNN), eXtreme Gradient Boosting (XGBoost), Gaussian Naïve Bayes (GNB), Quadratic Discriminant Analysis (QDA), Linear Discriminant Analysis (LDA), and Logistic Regression (LR). Local Interpretable Model-agnostic Explanations (LIME) is used to determine which features have the biggest impact on a specific prediction. LIME gives insights into the factors that influence the model’s decision. Random Forest and Adaptive Boosting classifiers exhibit superior performance with an accuracy of 99.2% and 98.9%, respectively.

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Revolutionizing Brain Tumor Analysis: Unleashing the Potential of Residual UNet for Enhanced Segmentation Accuracy

Revolutionizing Brain Tumor Analysis: Unleashing the Potenti...

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Advance computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： Pradeep Kumar Tiwari Prashant Johri School of Computing Science and Engineering Galgotias University Greater Noida India

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

Advances in diagnosis have been made, leading to significant changes in the approach to mental health diagnosis. This update delivers the powerful segmentation algorithms needed to provide accurate diagnosis and treatment planning. Within the scope of this research, a new technology that uses the Residual UNet deep learning architecture is proposed to increase segmentation accuracy. Our method solves the problem of incompleteness while ensuring that the model captures the correct data. This is done by taking advantage of the fixed connection available in the UNet design. The results show that this method is more efficient than other methods. This is done through long-term experiments on large datasets, including multimodal neuroimaging. An unlikely oncology surrogate is Residual UNet, which has proven to be accurate and useful in most cases. Specifically, our strategy achieved 0.9897 accuracy, 0.9726 accuracy, 0.9985 accuracy, and 0.8665 accuracy. BraST 2020 profile is used for this purpose. Additionally, our study proves that Residual UNet exceeds standards for brain tumors. This shows that it can provide superior segmentation for existing and specific technologies.

关键词： Training Neuroimaging Image segmentation Accuracy Three-dimensional displays Computer architecture Oncology

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