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Computer Software 2025年第1期42卷 3-22页

作者： Tsunoda, Kazuhiro Masuhara, Hidehiko Cong, Youyou Dept. of Mathematical and Computing Science Tokyo Institute of Technology Institute of Science Tokyo since October 2024 Japan

Enbugging quiz is a format of programming exercises that let learners edit a given program so that it yields a designated error message. In order to formulate a quiz problem with less ambiguity and reasonable difficulty, one needs to consider many variants of a program and the produced errors. In this article, we propose two methods of assisting the problem formulation processes, namely a code minimization method that preserves error messages and an efficient method of searching alternative answers based on syntax-directed code generation. We also report the results of the application of the methods to the quiz problems that the authors composed for a university course in order to assess the effectiveness of the methods. © 2025 Japan Society for Software science and Technology. All rights reserved.

关键词： Program debugging

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ML-driven Emotion Identification for Feedback Analysis in E-learning Platforms

ML-driven Emotion Identification for Feedback Analysis in E-...

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2024 OPJU International Technology Conference on Smart computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Chitteti, Chengamma Kopparam, Runvika Ganesh, B.V.S.S. Sutraya, Sukerthi Kamakshi, V. Jangam, Shiridinath School of Computing Dept. of Data Science Tirupati India Dept. of Csse Tirupati India G. Narayanamma Institute of Technology and Science for Women Dept. of Data Science Hyderabad India

ISBN: (纸本)9798350373783

With an emphasis on the cognitive and emotional effects of e-learning platforms, this study fills a research gap concerning user experience studies. The study suggests a nonintrusive technique to evaluate the psychological impacts of e-learning platforms by considering feedback data of users in the form of videos and analyzes emotions from video and decides the user feedback level. This process automates the understanding of feedback in a short amount of time for anyone, without requiring specific knowledge of the system. Feedback analysis for E-Learning platforms using deep learning methods for facial expression analysis is proposed in this work. Convolutional Neural Networks (CNNs), a category of deep learning, has become increasingly popular for its ability to precisely analyze and comprehend emotions from facial photos. These models may efficiently learn and extract essential features by being trained on large datasets of tagged facial expression photos. This application takes video feedback as input for various E-learning platforms and analyzes emotions and displays the predicted emotions on video. A publicly accessible dataset is used to test the proposed technique, and the results show that it achieves 90.3% accuracy. This indicates that the approach is highly effective in accurately recognizing facial emotions. © 2024 IEEE.

关键词： Contrastive Learning

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Predicting Employee Churn- data Driven Approach for Retention Management

Predicting Employee Churn- Data Driven Approach for Retentio...

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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Jayaraj, R. Mehrotra, Safal Kumar, Harshit School of Computing Srm Institute of Science and Technology Dept. of Data Science and Business Systems Chennai India

ISBN: (纸本)9798331510022

Employee churn is a critical challenge faced by organizations across industries, leading to disruptions in productivity and increased costs associated with recruitment and training. In this study, we propose an effective solution leveraging Random Forests to predict employee churn accurately and efficiently. Our approach addresses the inherent complexities of employee turnover prediction while considering resource constraints commonly encountered in practical applications. The proposed model incorporates feature engineering techniques and ensemble learning principles to harness the predictive power of Random Forests. Through a comprehensive evaluation on real-world employee datasets, we demonstrate the superior performance of our model in comparison to existing state-of-the-art methods. Our analysis encompasses various evaluation metrics, including accuracy, precision, recall, and F1-score, showcasing the robustness and reliability of our model across diverse organizational contexts. © 2024 IEEE.

关键词： Contrastive Learning

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Move Match: Live Dance Motion Monitoring and Feedback System

Move Match: Live Dance Motion Monitoring and Feedback System

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Saral, G. Baby Subasri, S. Ramya Shree, R. Bose, K. Regin Rajalakshmi Institute of Technology Dept. Artificial Intelligence & Data Science Chennai India Artificial Intelligence and Data Science Rajalakshmi Institute of Technology Chennai India Artificial Intelligence and Data Science Student Rajalakshmi Institute of Technology Chennai India Rajalakshmi Institute of Technology Dept. AI&DS & Cloud Computing Chennai India

ISBN: (纸本)9798331512965

Dance movement accuracy prediction is a critical component of modern dance training, yet traditional methods often lack objectivity due to the subjective nature of evaluations and the complexity of human movement. To address these challenges this paper introduces MoveMatch, which is a web-based platform built for real-time dance movement prediction. By leveraging deep learning techniques, the platform performs posture estimation in real-time and detects key points. The system compares the detected poses using Euclidean distance to calculate the alignment between corresponding key points which provides a quantitative measure of the user's accuracy. Based on this comparison, MoveMatch delivers real-time feedback, offering an accuracy score and personalized suggestions for improvement. This immediate feedback fosters a more efficient and engaging learning experience for dancers. Extensive testing of MoveMatch in various dance styles demonstrates its effectiveness in objectively assessing performance, enhancing both training quality and accessibility. The integration of pose estimation, deep learning and real-time comparison offers a novel, user-friendly solution for dancers seeking to improve their skills, transforming traditional dance education with AI-driven insights. © 2025 IEEE.

关键词： Deep learning

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Forecasting Air Pollution of Urban Chennai Localities using Artificial Intelligence 3

Forecasting Air Pollution of Urban Chennai Localities using ...

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3rd International Conference on Applied Artificial Intelligence and computing, ICAAIC 2024

作者： Faiz, Rayaan Agarwal, Tisha Rajeswari, D. Srm Institute of Science and Technology School of Computing College of Engineering and Technology Dept. of Data Science and Business Systems Chennai603203 India

ISBN: (纸本)9798350375190

India's air quality deteriorated significantly in 2023, ranking third worst globally, highlighting the urgency for effective monitoring and mitigation measures. To comprehend past trends and predict future patterns, historical air pollution data from seven urban areas in Chennai was analyzed using the XGBoost Algorithm. This study aims to gain insights into Chennai's air quality, identify abnormalities, and issue timely warnings to the public and governing authorities. XGBoost was employed to forecast AQI, NO2, PM2.5, NOx, PM10, CO, SO2, NH3, and Ozone levels, outperforming traditional LSTM, Prophet, and ARIMA models. Evaluation using RMSE and MAE scores revealed XGBoost's efficacy, with scores of 20.65 and 14.35, respectively. The research highlights regions like Alandur, Arumbakkam, and Manali with notable concentrations of contaminants, emphasizing the need for immediate action. These findings highlight the significance of utilizing advanced algorithms for accurate air quality forecasting and proactive pollution management. © 2024 IEEE.

关键词： Air quality

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Movie Reviews using Sentiment Analysis with Natural Language API 8

Movie Reviews using Sentiment Analysis with Natural Language...

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8th IEEE International Conference on science, Technology, Engineering and Mathematics, ICONSTEM 2023

作者： Athish, V.P. Rajeswari, D. Sree Nandha, S. Srm Institute of Science and Technology School of Computing Dept. of Data Science and Business Systems Tamilnadu Kattankulathur India

ISBN: (纸本)9798350347791

Sentiment Analysis is the process of analyzing sentiment of a piece of text by identifying polarity or writer's attitude towards any topic or product such as positive, neutral or negative. This paper uses sentiment analysis on movie reviews in twitter data, which is the most dominant public social media and its data is accessible using their own Twitter API. Words and misspellings make sentiment analysis for twitter data, harder due to the existence of slang words and bad spelling. This work uses Hadoop for the various features it provides over the API. The proposed work uses the knowledge base approach using lexica analyzer for sentiment analysis of movie reviews. The assigned score for movies is 100. The proposed work get all the positive, negative and neutral comments to judge "how good"or "how bad"the movie is. © 2023 IEEE.

关键词： Sentiment analysis

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Predicting Air Quality: Ensemble Learning Approach 2

Predicting Air Quality: Ensemble Learning Approach

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2nd International Conference on data science and Information System, ICDSIS 2024

作者： Ida Seraphim, B. Nagarajan, Esha Pathak, Shatakshi Srm Institute Of Science And Technology School Of Computing Dept. Of Computing Technologies Kattankulathur India

ISBN: (纸本)9798350381665

Air pollution is a rising concern, particularly in urban areas like Delhi, India, where the Air Quality Index (AQI) serves as a vital measure of environmental health. In the ongoing pursuit of refining air quality prediction, we are embarking on an innovative approach to predict Air Quality Index, integrating the power of ensemble learning. Unlike traditional single-model approaches, our work centers on the utilization of a Meta Classifier, a sophisticated ensemble technique that amalgamates the unique predictive abilities of each constituent model, synthesizing their outputs to achieve a superior performance. We employ a comprehensive methodology that includes data pre-processing, feature engineering, model training, and evaluation. The findings demonstrate a high degree of accuracy and reliability in forecasting AQI categories. This cutting-edge approach involves harnessing the collective intelligence of multiple machine learning models to enhance prediction accuracy and reliability. This research aims to contribute to improving air quality monitoring, offering valuable insights for environmental and public health management. © 2024 IEEE.

关键词： Time series analysis

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Glaucoma Detection through Deep Learning: A Transfer Learning Techniques using CDR Feature Extraction 3

Glaucoma Detection through Deep Learning: A Transfer Learnin...

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3rd IEEE International Conference on Distributed computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Vijayalakshmi, M. Basak, Debanjan Agarwal, Pragya Dept. Of Computing Technologies School Of Computing Srm Institute Of Science And Technology Kattankulathur India

ISBN: (纸本)9798350318609

Elevated intraocular pressure is the hallmark of glaucoma, which can cause vision loss and optic nerve damage if initially undiagnosed due to mild symptoms. For vision impairment to be effectively managed and prevented, early detection is essential. Optical coherence tomography and fundus imaging are used in traditional diagnosis;thus, the optic cup-to-disk ratio is a crucial parameter that needs precise segmentation. This work uses the ensemble technique of ResNet101, MobileNet, and a refined VGGNet-19 model for subtle structural learning from RGB fundus images and spatial coordinates. The approach addresses challenges related to glaucoma categorization as well as optic cup/disk segmentation. Overall accuracy on the test set was 94%. VGG19 scored 0.92, 0.95, and 0.93 F1-score, while ResNet101 scored 0.93, 0.94, and 0.935, and MobileNet scored a competitive 0.915 F1-score. This work highlights the clinical potential and significance of reliable early screening for the management of this vision-threatening disorder by demonstrating how segmentation and deep learning approaches can be used to enable effective automated glaucoma diagnosis. © 2024 IEEE.

关键词： Image segmentation

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Enhancing Efficiency of Wind Farm Energy using Digital Twin Technology 15

Enhancing Efficiency of Wind Farm Energy using Digital Twin ...

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

作者： Daniel, Jesline Kulkarni, Harshit Ravindra Mahajan, Sanchi Bhardwaj, Sugam Rahman, Habibur Agrawal, Shikhar SRM Institute of Science and Technology School of Computing Dept. of Computing Technologies Kattankulathur India

ISBN: (纸本)9798350370249

Using digital twin technology, the projectpresents a novel technique for wind farm monitoring and maintenance. This technique seeks to revolutionize wind farm management by assuring optimal performance, efficiency, and lifespan. The use of digital twin technology provides a virtual duplicate of the complete wind farm that isconstantly updated with real-Time data from sensors located around the facility. This allows for a highly accurate depiction of physical assets and their operating condition, supporting real-Time performance evaluation, predictivemaintenance, problem detection, energy optimization, and environmental impact reduction. The digital twin provides instantaneous performance evaluations by using sensor data, detecting underperforming turbines and forecasting possible breakdowns. It also plans maintenance tasks based on historical and real-Time data, reducing downtime and operational costs while increasing dependability and safety. The digital twin improves defect detection by leveraging modern analytics and machine learning, hence increasing theoverall dependability of the wind farm. © 2024 IEEE.

关键词： Predictive maintenance

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Apis Insight for Phenotype Classification and Hive Health Forecasting using IoT and Deep Learning

Apis Insight for Phenotype Classification and Hive Health Fo...

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2024 International Conference on Knowledge Engineering and Communication Systems, ICKECS 2024

作者： Singh, Ritvik Kumar Singh, Tanya Arulmurugan, A. Srm Institute of Science and Technology School of Computing Dept. of Computing Technologies Kattankulathur India

ISBN: (纸本)9798350359688

This research illuminates the pivotal quandary of bee health classification native to the Indian subcontinent, a crucial facet of maintaining agroecological equilibrium. Through a multifaceted integration of advanced computer vision, intricate deep learning paradigms, and hardware monitoring, the study explores the application of cutting-edge technologies to monitor and assess bee health and subspecies as well as beehive assessment. By incorporating hardware-based monitoring systems, the research exemplifies the vanguard potential of technologically entrenched methodologies in the realm of agricultural sustainability. This approach not only addresses the complexities of bee phenotype classification within the specified geographical context but also emphasizes the utilization of hardware components for real-time environmental data collection, fostering a paradigmatic shift in the axiomatic fulcrum of agro-symbiotic consilience. © 2024 IEEE.

关键词： agricultural sustainability agro-symbiotic consilience computer vision deep learning hardware monitoring systems

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