Relevant bibliographies by topics / Scalable Feedback Classification

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Academic literature on the topic 'Scalable Feedback Classification'

Author: Grafiati
Published: 5 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "Scalable Feedback Classification"

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Reseacher. "LEVERAGING GENERATIVE AI FOR EFFICIENT MOBILE APP FEEDBACK CLASSIFICATION AND QUALITY IMPROVEMENT." International Journal of Computer Engineering and Technology (IJCET) 15, no. 4 (2024): 444–51. https://doi.org/10.5281/zenodo.13285291.

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This paper introduces an innovative approach to mobile app user feedback analysis by harnessing the power of Generative AI technologies. We present an integrated system architecture that seamlessly combines an API-based scheduler for comprehensive data collection, a cutting-edge Generative AI classifier for nuanced feedback categorization, robust database integration for efficient data management, and an interactive visualization module for actionable insights. Our system goes beyond traditional classification methods by not only categorizing feedback based on predefined themes but also dynami
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Chebrolu, Madhusudhanarao. "Real-Time Feedback Signal Processing: Transforming Customer Surveys into Actionable Intelligence Through NLP-Driven Architectures." International Journal of Computing and Engineering 7, no. 8 (2025): 51–59. https://doi.org/10.47941/ijce.2942.

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Traditional survey mechanisms fail to meet the speed and scale requirements of modern customer-centric organizations, creating critical gaps between customer expression and business response. This work presents a comprehensive real-time feedback loop architecture that transforms passive survey data into intelligent, actionable signals through integrated NLP-based analysis, structured moderation logic, and automated decision routing. The proposed system leverages streaming data ingestion pipelines, multi-tier sentiment and intent classification models, and domain-specific moderation engines to
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Akter, Pinky, Safayet Hossain, Md Tarake Siddique, et al. "Sentiment Analysis of Consumer Feedback and Its Impact on Business Strategies by Machine Learning." American Journal of Applied Sciences 07, no. 01 (2025): 6–16. https://doi.org/10.37547/tajas/volume07issue01-02.

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Sentiment analysis is a powerful tool for transforming consumer feedback into actionable insights, enabling businesses to refine strategies and improve customer experiences. This study evaluates the performance of machine learning models, including Logistic Regression, Random Forest, SVM, LSTM, and BERT, for sentiment classification on a diverse dataset of customer reviews. BERT outperformed other models, achieving an AUC-ROC of 0.97 and an accuracy of 94.2%, showcasing its ability to capture complex semantic patterns in text. The findings provide businesses with critical insights into consume
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Liu, Chunyu. "Research on Library Book Information Resource Management Based on Artificial Intelligence and Sensors." Journal of Sensors 2022 (April 13, 2022): 1–10. http://dx.doi.org/10.1155/2022/3720811.

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In order to explore the research on library book information resource management, the author proposes a method based on artificial intelligence and sensors. Using an improved SVM algorithm, in order to realize the personalized data mining of the library, the support vector machine algorithm has supervised, scalable, and nonlinear high-efficiency characteristics in the use process, able to achieve nonlinear multicore data clustering effect, thereby improving the learning ability of data mining. The experimental results show the following: BP neural network was used to adaptively train the proce
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Al Tawil, Arar, Hanaa Fathi, Sharaf Alzoubi, Amneh Shaban, and Laiali H. Almazaydeh. "Advanced Feature Extraction and Machine Learning Techniques for Classifying Steam Game Feedback." International Journal of Interactive Mobile Technologies (iJIM) 19, no. 01 (2025): 107–25. https://doi.org/10.3991/ijim.v19i01.51237.

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The gaming industry produces vast amounts of user-generated feedback, making it challenging for developers to efficiently analyze and respond to real-time reviews. This study addresses the problem of classifying large-scale unstructured user feedback from Steam reviews. In this paper an approach that integrates traditional machine learning models and deep learning models is proposed. XGBoost is used to manage feature-rich datasets, reducing overfitting. Long-short-term memory (LSTM) and Bi-directional LSTM are used to enhance the accuracy and robustness of classification. Feature extraction te
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Ravi Sankar Sambangi. "Enhancing Automotive Safety through Context-Aware Ontology Classification." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 1 (2025): 1737–46. https://doi.org/10.32628/cseit251112143.

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Dynamic contextual feature extraction has emerged as a critical approach for classifying unstructured automotive safety data into domain-specific ontologies. This article presents a novel framework that leverages part-of-speech tagging, positional probabilities, and optimized feature vectors to process diverse safety datasets effectively. This methodology introduces adaptive context windows and domain-aware feature extraction techniques, demonstrating marked improvements in classification accuracy compared to traditional approaches. This article shows a substantial enhancement in pattern recog
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G. Anish Kumar and Dr. C Jayapratha. "Twitter Sentiment Analysis Using Machine Learning Techniques." International Journal of Scientific Research in Science and Technology 12, no. 4 (2025): 01–04. https://doi.org/10.32628/ijsrst251241.

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This paper presents an effective sentiment analysis system designed to classify the polarity of tweets into positive, negative, or neutral sentiments. The framework utilizes supervised machine learning algorithms, including Logistic Regression, Support Vector Machines (SVM), and Random Forest, trained on the Sentiment140 dataset. Text preprocessing techniques such as tokenization, stopword removal, stemming, and TF-IDF vectorization are applied to improve classification performance. The proposed system achieves an accuracy of 87.2% with SVM, outperforming other baseline models. This solution o
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Jayathissa, Prageeth, Matias Quintana, Mahmoud Abdelrahman, and Clayton Miller. "Humans-as-a-Sensor for Buildings—Intensive Longitudinal Indoor Comfort Models." Buildings 10, no. 10 (2020): 174. http://dx.doi.org/10.3390/buildings10100174.

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Evaluating and optimising human comfort within the built environment is challenging due to the large number of physiological, psychological and environmental variables that affect occupant comfort preference. Human perception could be helpful to capture these disparate phenomena and interpreting their impact; the challenge is collecting spatially and temporally diverse subjective feedback in a scalable way. This paper presents a methodology to collect intensive longitudinal subjective feedback of comfort-based preference using micro ecological momentary assessments on a smartwatch platform. An
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Mishra, Yashraj. "AI Human Fitness Tracker using Computer Vision with MediaPipe." International Journal for Research in Applied Science and Engineering Technology 13, no. 5 (2025): 1659–69. https://doi.org/10.22214/ijraset.2025.70547.

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In recent years, the integration of artificial intelligence (AI) into health and fitness domains has significantly enhanced personal training and physical wellness monitoring. This research introduces an AI-powered Fitness Tracker system that utilizes computer vision and pose estimation techniques to detect human body posture and accurately count repetitions or steps for various physical exercises. The system leverages MediaPipe for real-time human pose detection, computing joint angles to analyse movements and classify exercises such as push-ups, pull-ups, squats, sit-ups, and walking. It inc
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Tu, Bui Minh, Nguyen Minh Tu, and Le Hoang Nam. "Development of Automatic Assessment System Based on Machine Learning for Student Learning Evaluation." Al-Hijr: Journal of Adulearn World 3, no. 4 (2025): 483–93. https://doi.org/10.55849/alhijr.v3i4.856.

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The rapid advancement of machine learning (ML) has significantly impacted educational technologies, particularly in the area of student assessment. Traditional assessment methods often require substantial time and resources, and may not provide immediate or personalized feedback. An automatic assessment system based on machine learning can offer an efficient solution by automating the evaluation process and providing real-time, data-driven insights into student performance. This study explores the development of an automatic assessment system using machine learning algorithms to evaluate stude
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Book chapters on the topic "Scalable Feedback Classification"

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Klein, Julia, and Tatjana Petrov. "Understanding Social Feedback in Biological Collectives with Smoothed Model Checking." In Leveraging Applications of Formal Methods, Verification and Validation. Adaptation and Learning. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-19759-8_12.

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AbstractBiological groups exhibit fascinating collective dynamics without centralised control, through only local interactions between individuals. Desirable group behaviours are typically linked to a certain fitness function, which the group robustly performs under different perturbations in, for instance, group structure, group size, noise, or environmental factors. Deriving this fitness function is an important step towards understanding the collective response, yet it easily becomes non-trivial in the context of complex collective dynamics. In particular, understanding the social feedback
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Abedini Mani, von Cavallar Stefan, Chakravorty Rajib, Davis Matthew, and Garnavi Rahil. "A Cloud-Based Infrastructure for Feedback-Driven Training and Image Recognition." In Studies in Health Technology and Informatics. IOS Press, 2015. https://doi.org/10.3233/978-1-61499-564-7-691.

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Advanced techniques in machine learning combined with scalable “cloud” computing infrastructure are driving the creation of new and innovative health diagnostic applications. We describe a service and application for performing image training and recognition, tailored to dermatology and melanoma identification. The system implements new machine learning approaches to provide a feedback-driven training loop. This training sequence enhances classification performance by incrementally retraining the classifier model from expert responses. To easily provide this application and
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Arel Itamar and Berant Shay. "Application Feedback in Guiding a Deep-Layered Perception Model." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2010. https://doi.org/10.3233/978-1-60750-661-4-4.

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Deep-layer machine learning architectures continue to emerge as a promising biologically-inspired framework for achieving scalable perception in artificial agents. State inference is a consequence of robust perception, allowing the agent to interpret the environment with which it interacts and map such interpretation to desirable actions. However, in existing deep learning schemes, the perception process is guided purely by spatial regularities in the observations, with no feedback provided from the target application (e.g. classification, control). In this paper, we propose a simple yet power
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Dewangan, Omprakash. "MULTIMODAL DATA SOURCES IN SENTIMENT ANALYSIS." In Futuristic Trends in Information Technology Volume 3 Book 2. Iterative International Publisher, Selfypage Developers Pvt Ltd, 2024. http://dx.doi.org/10.58532/v3bfit2p3ch4.

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Sentiment analysis, the process of identifying and extracting subjective information from textual data, has gained significant attention in various domains such as social media analysis, customer feedback analysis, and market research. Traditional sentiment analysis approaches primarily rely on textual data alone, neglecting the rich information contained in other modalities such as images, videos, and audio. However, the advent of social media platforms and the widespread availability of multimedia content have highlighted the importance of considering multimodal data sources for a more compr
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Conference papers on the topic "Scalable Feedback Classification"

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Enrique Bances P, Nelson, Urs Schneider, Thomas Bauernhansl, and Jörg Siegert. "Enhancing Ergonomics in Construction Industry Environments: A Digital Solution with Scalable Event-Driven Architecture." In 15th International Conference on Applied Human Factors and Ergonomics (AHFE 2024). AHFE International, 2024. http://dx.doi.org/10.54941/ahfe1005358.

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The construction sector remains among the least digitized and automated industries, where human cognitive intervention is still necessary for many tasks. These tasks often entail significant physical exertion, increasing the risk of Musculoskeletal Disorders (MSDs) when workers perform unexpected movements or events. While assessment methods and technologies like wearable devices, bio-signal sensors, and digital tools enable real-time monitoring of ergonomic factors, integrating them simultaneously presents a challenge. This paper describes developing and implementing an event-based architectu
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Trivedi, Shubham, and Ramya Hebbale Ramkumar. "Applying AI to Reduce Software Testing Defects’ Rejection." In 11th SAEINDIA International Mobility Conference (SIIMC 2024). SAE International, 2024. https://doi.org/10.4271/2024-28-0188.

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<div class="section abstract"><div class="htmlview paragraph">The efficiency and accuracy of defect control are critical components in software testing, as they determine the final product's quality and cost. Rejection of defects for various reasons, like non-reproducibility, erroneous classification or inadequate information, is one of the largest issues that testers face. This paper presents an AI-driven approach that reduces the number of defect rejections by using the past defect data to give testers real-time advises and warnings.</div><div class="htmlview paragraph"&
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Abougrad, Hisham, Manasa Yegamati, and Mimi Mather. "Hybrid Deep Learning Healthcare AI Framework for Real-Time Human Pose Estimation and Remote Patient Monitoring to Support TKR Physiotherapy." In Human Interaction and Emerging Technologies (IHIET-FS 2025): Future Systems and Artificial Intelligence Applications. AHFE International, 2025. https://doi.org/10.54941/ahfe1005962.

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Total Knee Replacement (TKR) rehabilitation critically depends on precise physiotherapy exercise execution, and the rise of patient volumes and constrained clinical resources limit continuous supervision. This study presents an Artificial Intelligence (AI) framework for real-time assessment and feedback of TKR exercises using deep learning–based human pose estimation to empower remote rehabilitation. We investigate three architectures: a Dense Convolutional Neural Network (DCNN) incorporating frame decoupling for robust joint tracking; a pruned Generative Adversarial Network (Sparse GAN) optim
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Abougrad, Hisham, Manasa Yegamati, and Mimi Mather. "Hybrid Deep Learning Healthcare AI Framework for Real-Time Human Pose Estimation and Remote Patient Monitoring to Support TKR Physiotherapy." In Human Interaction and Emerging Technologies (IHIET-FS 2025): Future Systems and Artificial Intelligence Applications. AHFE International, 2025. https://doi.org/10.54941/ahfe10059725962.

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Total Knee Replacement (TKR) rehabilitation critically depends on precise physiotherapy exercise execution, and the rise of patient volumes and constrained clinical resources limit continuous supervision. This study presents an Artificial Intelligence (AI) framework for real-time assessment and feedback of TKR exercises using deep learning–based human pose estimation to empower remote rehabilitation. We investigate three architectures: a Dense Convolutional Neural Network (DCNN) incorporating frame decoupling for robust joint tracking; a pruned Generative Adversarial Network (Sparse GAN) optim
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