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Journal articles on the topic 'Scalable Feedback Classification'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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1

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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Urvashi, Urvashi, Syed Wajahat Abbas Rizvi, Dwivedi P.K., Ashish Kumar Pandey, and Sandhya Sandhya. "DEEP LEARNING-BASED MENTAL HEALTH DETECTION USING FINE-TUNED BERT: A MULTICLASS TEXT CLASSIFICATION APPROACH." Journal of Dynamics and Control 9, no. 5 (2025): 208–15. https://doi.org/10.71058/jodac.v9i5018.

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Mental health disorders, including anxiety, bipolar disorder, and suicidal tendencies, significantly affect individual well-being and necessitate timely detection for effective intervention. Traditional assessment methods, such as clinical evaluations and self-reported surveys, are often time-consuming and subjective. This paper introduces a deep learning-based approach utilizing a fine-tuned BERT (Bidirectional Encoder Representations from Transformers) model for multi-class mental health classification through textual analysis. The system classifies text into four categories—depression, anxi

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Jin, Jonathan. "Harnessing the Power of Large Language Models for Real-World Sentiment Classification on Social Media." Advances in Economics, Management and Political Sciences 156, no. 1 (2025): 214–24. https://doi.org/10.54254/2754-1169/2025.20662.

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Social media provides an abundant source of customer feedback, making sentiment analysis critical for businesses to understand consumer preferences and inform strategies. This study evaluates the effectiveness of Large Language Models (LLMs), such as GPT-3.5, GPT-4, and Google Gemini, in sentiment classification compared to traditional machine learning (ML) and deep learning (DL) models. Two benchmark datasets, the Stanford NLP/IMDB dataset and the FinanceInc/Auditor Sentiment dataset, are used to assess performance on general and domain-specific tasks. Advanced techniques, including zero-shot

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Ugendhar, A., Babu Illuri, Sridhar Reddy Vulapula, et al. "A Novel Intelligent-Based Intrusion Detection System Approach Using Deep Multilayer Classification." Mathematical Problems in Engineering 2022 (May 6, 2022): 1–10. http://dx.doi.org/10.1155/2022/8030510.

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Cybersecurity in information technology (IT) infrastructures is one of the most significant and complex issues of the digital era. Increases in network size and associated data have directly affected technological breakthroughs in the Internet and communication areas. Malware attacks are becoming increasingly sophisticated and hazardous as technology advances, making it difficult to detect an incursion. Detecting and mitigating these threats is a significant issue for standard analytic methods. Furthermore, the attackers use complex processes to remain undetected for an extended period. The ch

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J, Jennifa. "Reinforcement Learning for Intrusion Detection: More Model Longness and Fewer Update." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem49002.

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Abstract - In today’s dynamic cybersecurity landscape, Intrusion Detection Systems (IDS) must adapt to evolving threats without relying on frequent retraining or compromising performance. This project proposes a novel IDS framework that synergistically combines Convolutional Neural Networks (CNNs) with Reinforcement Learning (RL) to classify network traffic as normal or anomalous, while minimizing the frequency of model updates. The framework is deployed via an intuitive, Streamlit-based web interface that supports real-time predictions based on user-provided network feature inputs.A pre-train

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Siddiqi, Shafaq, Roman Kern, and Matthias Boehm. "SAGA: A Scalable Framework for Optimizing Data Cleaning Pipelines for Machine Learning Applications." Proceedings of the ACM on Management of Data 1, no. 3 (2023): 1–26. http://dx.doi.org/10.1145/3617338.

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In the exploratory data science lifecycle, data scientists often spent the majority of their time finding, integrating, validating and cleaning relevant datasets. Despite recent work on data validation, and numerous error detection and correction algorithms, in practice, data cleaning for ML remains largely a manual, unpleasant, and labor-intensive trial and error process, especially in large-scale, distributed computation. The target ML application---such as classification or regression models---can be used as a signal of valuable feedback though, for selecting effective data cleaning strateg

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Gupta, Neha, Aashi Sharma, Suneet Shukla, and Praveen Kumar. "AI-based mock interview evaluator: An emotion and confidence classifier model." International Journal of Research in Engineering and Innovation 09, no. 03 (2025): 136–42. https://doi.org/10.36037/ijrei.2025.9308.

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In today’s highly competitive job market, interview preparedness has become more critical than ever. Traditional mock interviews often lack scalability, objectivity, and actionable insights. To address these limitations, this paper proposes an AI-Based Mock Interview Evaluator that combines emotion recognition and confidence classification to assess candidate performance. Using facial expression analysis and speech feature extraction, the system offers real-time feedback on emotional state and confidence level during simulated interview sessions. The model leverages Convolutional Neural Networ

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Laganà, Filippo, Diego Pellicanò, Mariangela Arruzzo, Danilo Pratticò, Salvatore A. Pullano, and Antonino S. Fiorillo. "FEM-Based Modelling and AI-Enhanced Monitoring System for Upper Limb Rehabilitation." Electronics 14, no. 11 (2025): 2268. https://doi.org/10.3390/electronics14112268.

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The integration of physical modelling, artificial intelligence (AI), and embedded electronics represents a promising direction in the development of intelligent systems for rehabilitation monitoring. Most existing approaches, however, treat biomechanical simulation and sensor-based AI separately, without leveraging their potential synergy. This study introduces a hybrid framework for upper limb rehabilitation that combines finite element modelling (FEM), AI-based trend classification, and a custom-designed electronic system for real-time signal acquisition and wireless data transmission. A mec

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EMALETDINOVA, L. YU, and M. S. PISHCHULIN. "TEXT SENTIMENT CLASSIFICATION BASED ON NEURAL NETWORK MODELING." Herald of Technological University 28, no. 6 (2025): 91–95. https://doi.org/10.55421/3034-4689_2025_28_6_91.

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In this work, we propose a method for automatically determining the sentiment of text reviews posted by online store customers using recurrent neural networks equipped with two LSTM blocks with 1024 and 128 neurons, each employing ReLU activation. For text encoding into vectors, we use the FastText model, which captures the morphological nuances of the Russian language by extracting information from word sub-units. The two successive LSTM layers enable modeling of long-term contextual dependencies, crucial for analyzing textual data. This study aims to overcome the limitations of existing meth

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Derbentsev, Vasyl, Vitalii Bezkorovainyi, Renat Akhmedov, and Mykola Bondarchuk. "EVALUATING CUSTOMER EXPERIENCE IN E-COMMERCE: MULTILINGUAL SENTIMENT ANALYSIS OF USER REVIEWS USING TRANSFORMER MODELS." Смарт-економіка, підприємництво та безпека 2, no. 2 (2024): 59–70. https://doi.org/10.60022/sis.2.(02).6.

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This study addresses the challenge of multilingual sentiment analysis in e-commerce, with a focus on Ukrainian and Russian book reviews. We propose a hybrid framework based on transformer architectures that accounts for the linguistic complexity of Slavic languages and the significant class imbalance often present in customer feedback data. Using a dataset of approximately 70,000 user reviews from the Ukrainian online bookstore Yakaboo, we evaluate four model variants based on the XLM-RoBERTa architecture and compare their performance to a monolingual Ukrainian RoBERTa baseline. The classifica

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Gayatri Tavva. "Scalable data quality alerting powered by AI Models: Architecture and tooling for self-healing data pipelines." World Journal of Advanced Engineering Technology and Sciences 16, no. 1 (2025): 594–602. https://doi.org/10.30574/wjaets.2025.16.1.1235.

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The growing complexity and volume of contemporary data pipelines have boosted the significance of smart data quality monitoring infrastructures. The traditional rule-based techniques tend to fail or provide unreliable analytics in dynamic and high-throughput environments, causing silent failures. This review explores the possibility of artificial intelligence (AI) and machine learning (ML) leveraging the use of adaptive data quality alerting systems that can be implemented in scale. It gives importance to architecture concepts, model approaches, and tooling environments that help in anomaly de

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Setiawan, Retno Agus, Rosyid Ridlo Al-Hakim, Indah Trivilia, and Ulan Juniarti. "Design and Development of Drug Formulation Classification Information System." Rekayasa 17, no. 3 (2024): 437–48. https://doi.org/10.21107/rekayasa.v17i3.28018.

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The increasing complexity of pharmaceutical management in pharmacies demands efficient systems to streamline drug classification and inventory control. As pharmacies handle a diverse range of formulations, including tablets, syrups, and injectables, the need for advanced systems that ensure accuracy, efficiency, and compliance is becoming increasingly evident. This study focuses on the design and development of a web-based Drug Formulation Classification Information System, specifically tailored to improve the management of drug formulations. The system was developed using a Rapid Application

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Devika, D. Nair. "EMOTRAX: A MULTI-MODAL AI-POWERED EMOTION RECOGNITION SYSTEM FOR REAL-TIME MENTAL HEALTH SUPPORT USING TEXT AND VOICE INPUTS." International Journal of Advances in Engineering & Scientific Research 12, no. 2 (2025): 35–46. https://doi.org/10.5281/zenodo.15428831.

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<em>With rising mental health concerns, there is an urgent need for intelligent digital tools that provide empathetic and timely support. This paper introduces EmoTrax, a multi-modal emotion recognition system that leverages AI and NLP to detect emotional states in real time. It processes both text and voice inputs to generate personalized mental health recommendations using deep learning models. The system incorporates speech-to-text conversion and sentiment classification to ensure accuracy and contextual relevance. EmoTrax is designed with a user-friendly interface and a scalable backend fo

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Subha K, Benitlin. "Mental Stress Detection Using Wearable Sensors and Machine Learning." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47434.

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Abstract - This project proposes a real-time stress level detection system using a Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) layers. It collects biometric data—heart rate (BPM) and SpO₂ levels— from a pulse sensor connected to an ESP8266 microcontroller. The data is transmitted via Wi-Fi to a Python-based backend for processing. The collected signals are normalized usingMinMaxScaler and reshaped to preserve their sequential nature. The preprocessed data is fed into a trained RNN model that classifies stress levels into four categories: No Stress,Medium, High, and Very H

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Nayem Uddin Prince, Mohammed Nazmul Hoque Shawon, Zakaria Kamal Shahed, Riya Islam Nupur, Farzana Akther Mele, and Md Abdullah Al Mamun. "E-commerce clothing review analysis by advanced ML Algorithms." World Journal of Advanced Research and Reviews 24, no. 1 (2024): 1680–90. http://dx.doi.org/10.30574/wjarr.2024.24.1.3044.

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A significant aspect in the rapid ascent of Bangladesh's e-commerce sector in recent years has been the significance of consumer evaluations. By examining these reviews, readers can gain enhanced insight into consumer happiness and product quality. This research analyses the sentiment of online clothes reviews with advanced machine-learning techniques. The fundamental purpose of evaluating several machine learning models, such as KNN, RF, XGB, Multi, LSTM, and CNN, is to identify the most effective method for sentiment classification. Following the compilation of an extensive dataset of clothi

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Nayem, Uddin Prince, Nazmul Hoque Shawon Mohammed, Kamal Shahed Zakaria, Islam Nupur Riya, and Abdullah Al Mamun Md. "E-commerce clothing review analysis by advanced ML Algorithms." World Journal of Advanced Research and Reviews 24, no. 1 (2024): 1680–90. https://doi.org/10.5281/zenodo.15039250.

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A significant aspect in the rapid ascent of Bangladesh's e-commerce sector in recent years has been the significance of consumer evaluations. By examining these reviews, readers can gain enhanced insight into consumer happiness and product quality. This research analyses the sentiment of online clothes reviews with advanced machine-learning techniques. The fundamental purpose of evaluating several machine learning models, such as KNN, RF, XGB, Multi, LSTM, and CNN, is to identify the most effective method for sentiment classification. Following the compilation of an extensive dataset of clothi

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Wang, Renlong, Shiwei Zhang, Jinxia Sha, Bin Liu, Dasheng Zhang, and Boxin Wang. "A Hierarchical Water Supply–Demand Regulation Model Coupling System Dynamics and Feedback Control Mechanisms: A Case Study in Wu’an City, China." Water 17, no. 12 (2025): 1732. https://doi.org/10.3390/w17121732.

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Water scarcity has become a critical global challenge, particularly in rapidly developing regions where water demand often exceeds sustainable supply capacities. Traditional “demand-driven” water management approaches have proven inadequate to address this imbalance, necessitating the development of more sophisticated “supply-driven” solutions. This study presents a groundbreaking System Dynamics (SD)-Feedback-Hierarchical Water Demand (SD-F-HWD) model that advances water resources management through three contributions. First, the model substantially extends conventional water demand hierarch

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RAMASRI, SINGAMPALLI, and M. BALA NAGA BHUSHANAMU. "Proactive Measures of the Organization Regarding Employee Attrition Using Deep Learning." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 07 (2025): 1–9. https://doi.org/10.55041/ijsrem51623.

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Employee attrition remains a persistent concern for organizations, often leading to increased costs and disruptions in workforce planning. To address this issue, we present a predictive system that leverages deep learning to assess the likelihood of employee resignation. The proposed solution combines a PyTorch-based Multi-Layer Perceptron (MLP) with a Flask-powered web interface, enabling real-time, user-friendly attrition prediction. The model is trained on structured HR data that includes both numerical and categorical attributes related to employee demographics, job roles, and workplace be

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Marie Failing, Johanna Marie, José V. Abellán-Nebot, Sergio Benavent Benavent Nácher, Pedro Rosado Rosado Castellano, and Fernando Romero Romero Subirón. "A Tool Condition Monitoring System Based on Low-Cost Sensors and an IoT Platform for Rapid Deployment." Processes 11, no. 3 (2023): 668. http://dx.doi.org/10.3390/pr11030668.

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Tool condition monitoring (TCM) systems are key technologies for ensuring machining efficiency. Despite the large number of TCM solutions, these systems have not been implemented in industry, especially in small- and medium-sized enterprises (SMEs), mainly because of the need for invasive sensors, time-consuming deployment solutions and a lack of straightforward, scalable solutions from the laboratory. The implementation of TCM solutions for the new era of the Industry 4.0 is encouraging practitioners to look for systems based on IoT (Internet of Things) platforms with plug and play capabiliti

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Luo, Guohu. "Online Detection System for Ore Particle Size Distribution Based on Deep Learning." Journal of Computer, Signal, and System Research 2, no. 3 (2025): 1–16. https://doi.org/10.71222/r6qws842.

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This study presents a deep learning-based system for the online detection of ore particle size distribution (PSD) to enhance efficiency and enable real-time monitoring in mining operations. Traditional methods, such as sieving and manual sampling, are time-consuming, labor-intensive, and unsuitable for real-time applications. To address these limitations, a system was developed that integrates advanced computer vision techniques, robust hardware components, and intelligent software design. The system captures high-quality images of ore particles using industrial cameras and lighting systems, a

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Pasupuleti, Murali Krishna. "Adaptive Quantum Learning Architectures with Superalgebraic Entangled Layers." International Journal of Academic and Industrial Research Innovations(IJAIRI) 05, no. 04 (2025): 133–41. https://doi.org/10.62311/nesx/rp1025.

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This paper introduces a novel quantum machine learning framework that integrates adaptive learning capabilities with superalgebraically structured entangled layers. Leveraging the mathematical formalism of Lie superalgebras—specifically osp(1∣2) and sl(2∣1)—the proposed architecture encodes quantum states in graded Hilbert spaces and governs their evolution via symmetry-preserving supercharges. The entangled layers dynamically adapt to task-specific feedback through a hybrid quantum-classical training protocol that ensures algebraic consistency and quantum coherence. Symbolic computation and q

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Jayanth, Kapudasu. "Smart Fitness Guide: AI-Driven Fitness Guidance Platform." International Journal for Research in Applied Science and Engineering Technology 13, no. 7 (2025): 2526–31. https://doi.org/10.22214/ijraset.2025.73402.

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The Smart Sports Trainer is a pioneering web-based platform that leverages artificial intelligence to enhance fitness training through real-time posture analysis and tailored exercise plans. By integrating advanced computer vision, deep learning, and generative AI, the system accurately identifies body joint positions, evaluates exercise form, and provides actionable feedback to minimize injury risks and optimize performance. Built with TensorFlow, MediaPipe, and Django in a scalable, modular architecture, it processes images, videos, or live webcam streams via an intuitive interface. Employin

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Alaya, Bechir. "Payoff-based Dynamic Segment Replication and Graph Classification Method with Attribute Vectors Adapted to Urban VANET." ACM Transactions on Multimedia Computing, Communications, and Applications 17, no. 3 (2021): 1–22. http://dx.doi.org/10.1145/3440018.

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Due to the number of constraints and the dynamic nature of vehicular ad hoc networks (VANET), effective video broadcasting always remains a difficult task. In this work, we proposed a quality of video visualization guarantee model based on a feedback loop and an efficient algorithm for segmenting and replicating video segments using the Payoff-based Dynamic Segment Replication Policy (P-DSR). In the urban VANET environment, P-DSR is defined by taking into account the position of the vehicles, the speed, the direction, the number of neighboring vehicles, and the reputation of each node to stabi

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Karunamurthy, Dr A. "AI-Powered Resume Analysis Using SpaCy for Skill Extraction and Job Matching." International Scientific Journal of Engineering and Management 04, no. 05 (2025): 1–7. https://doi.org/10.55041/isjem03528.

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Abstract -In today's competitive job market, recruiters face the challenge of efficiently sifting through vast volumes of resumes to identify the best candidates for open positions. Traditional keyword-based filtering methods are often inadequate in identifying the nuances of skills and experiences required for specific job roles. This project presents an AI-powered resume analysis system using the SpaCy natural language processing (NLP) library to enhance the accuracy of resume screening and job matching. Leveraging SpaCy's advanced capabilities, including Named Entity Recognition (NER), sema

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Bagde, Sarvari. "Apple Disease Detection with Prediction Using Machine Learning and AI." International Journal of Advanced Research in Science and Technology 14, no. 5 (2025): 1565–69. https://doi.org/10.62226/ijarst2024132551.

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Severely impact crop yield and quality. Traditional detection methods rely on expert inspection, which is time-consuming, error-prone, and inaccessible for many farmers. This paper proposes a machine learning and AI-based system to detect apple diseases using image processing and classification techniques. By training deep learning models—particularly convolutional neural networks (CNNs)—on leaf and fruit images, the system aims to provide early, accurate, and automated disease detection. The solution also integrates IoT technology for real-time data communication and monitoring, offering a sc

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Alshardan, Amal, Hany Mahgoub, Saad Alahmari, Mohammed Alonazi, Radwa Marzouk, and Abdullah Mohamed. "Cloud-to-Thing continuum-based sports monitoring system using machine learning and deep learning model." PeerJ Computer Science 11 (February 10, 2025): e2539. https://doi.org/10.7717/peerj-cs.2539.

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Sports monitoring and analysis have seen significant advancements by integrating cloud computing and continuum paradigms facilitated by machine learning and deep learning techniques. This study presents a novel approach for sports monitoring, specifically focusing on basketball, that seamlessly transitions from traditional cloud-based architectures to a continuum paradigm, enabling real-time analysis and insights into player performance and team dynamics. Leveraging machine learning and deep learning algorithms, our framework offers enhanced capabilities for player tracking, action recognition

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Neha J. Zade. "Neural Architecture Search: Automating the Design of Convolutional Models for Scalability." Panamerican Mathematical Journal 34, no. 4 (2024): 178–93. http://dx.doi.org/10.52783/pmj.v34.i4.1877.

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Neural Architecture Search (NAS) has changed the way convolutional neural networks (CNNs) are designed and optimized by providing an automatic way to make models that are scalable and perform well. Traditional ways of making CNN designs depend on a lot of expert knowledge and a lot of hand tuning, which can make them less efficient and scalable. NAS simplifies this process by using powerful search algorithms to look through huge architecture areas and find the best models for each job. This method not only makes CNNs more scalable, but it also speeds up the planning process, which means that h

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Sufi, Fahim, and Musleh Alsulami. "Unmasking Media Bias, Economic Resilience, and the Hidden Patterns of Global Catastrophes." Sustainability 17, no. 9 (2025): 3951. https://doi.org/10.3390/su17093951.

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The increasing frequency and destructiveness of natural disasters necessitate scalable, transparent, and timely analytical frameworks for risk reduction. Traditional disaster datasets—curated by intergovernmental bodies such as EM-DAT and UNDRR—face limitations in spatial granularity, temporal responsiveness, and accessibility. This study addresses these limitations by introducing a novel, AI-enhanced disaster intelligence framework that leverages 19,130 publicly available news articles from 453 global sources between September 2023 and March 2025. Using OpenAI’s GPT-3.5 Turbo model for disast

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Almusharraf, Fahad, Jonathan Rose, and Peter Selby. "Engaging Unmotivated Smokers to Move Toward Quitting: Design of Motivational Interviewing–Based Chatbot Through Iterative Interactions." Journal of Medical Internet Research 22, no. 11 (2020): e20251. http://dx.doi.org/10.2196/20251.

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Background At any given time, most smokers in a population are ambivalent with no motivation to quit. Motivational interviewing (MI) is an evidence-based technique that aims to elicit change in ambivalent smokers. MI practitioners are scarce and expensive, and smokers are difficult to reach. Smokers are potentially reachable through the web, and if an automated chatbot could emulate an MI conversation, it could form the basis of a low-cost and scalable intervention motivating smokers to quit. Objective The primary goal of this study is to design, train, and test an automated MI-based chatbot c

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Çiçek, Selen, Mehmet Sadık Aksu, Emre Öztürk, et al. "Architectural Critique with Artificial Intelligence: Generating Architectural Reviews through Vision-Language Models." Journal of Computational Design 6, no. 1 (2025): 165–90. https://doi.org/10.53710/jcode.1618548.

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Artificial Intelligence (AI) offers a potent opportunity to rethink architectural critique, in cases such as architectural design competitions. The challenge lies in capturing the interpretive depth required for design evaluation—an inherently human process that connects intuition, reasoning, and contextual sensitivity. Building on this premise, the proposed approach uses a domain-specific dataset, curated and validated by experienced architects as domain experts, to train a context-aware Visual-Language Model (VLM) capable of delivering a nuanced critique. The model development follows two di

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Israel, Jacob Udoh, Oluwadamilare Ikechukwu Alabi, and Janet Faleye Oluranti. "Machine Learning-Based Automated Hemoparasite Detection (MLAHD) Model: A Mathematical Perspective." SSR Journal of Artificial Intelligence (SSRJAI) 2, no. 1 (2025): 7–23. https://doi.org/10.5281/zenodo.15484143.

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Hemoparasitic infections, particularly those caused by Plasmodium spp., pose significant health challenges worldwide, especially in resource-limited settings where traditional diagnostic methods are often inadequate. This study addresses critical gaps in existing diagnostic practices, including the reliance on labour-intensive microscopy and the lack of accessible, automated solutions. The aim is to develop and evaluate an innovative Machine Learning-Based Automated Hemoparasite Detection (MLAHD) model that integrates Convolutional Neural Networks (CNNs) with affordable Raspberry Pi systems to

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Al Ogaili, Riyadh Rahef Nuiaa, Osamah Adil Raheem, Mohamed H. Ghaleb Abdkhaleq, et al. "AntDroidNet Cybersecurity Model: A Hybrid Integration of Ant Colony Optimization and Deep Neural Networks for Android Malware Detection." Mesopotamian Journal of CyberSecurity 5, no. 1 (2025): 104–20. https://doi.org/10.58496/mjcs/2025/008.

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Malware detection is a vital problem, and efficient methods that can efficiently detect malware are needed. The increasing use of mobile computers makes malware detection a vital part of security in an era where smartphones have come to play a key role in many of our daily lives. Earlier approaches, however, suffer from high false positive rates; they are not scalable for larger databases, or they are not amenable to adapt well to novel zero-day malware. For these reasons, the demand for more sensitive and flexible detection models is high. In this study, we develop a hybrid mobile malware det

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Gkintoni, Evgenia, Hera Antonopoulou, Andrew Sortwell, and Constantinos Halkiopoulos. "Challenging Cognitive Load Theory: The Role of Educational Neuroscience and Artificial Intelligence in Redefining Learning Efficacy." Brain Sciences 15, no. 2 (2025): 203. https://doi.org/10.3390/brainsci15020203.

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Background/Objectives: This systematic review integrates Cognitive Load Theory (CLT), Educational Neuroscience (EdNeuro), Artificial Intelligence (AI), and Machine Learning (ML) to examine their combined impact on optimizing learning environments. It explores how AI-driven adaptive learning systems, informed by neurophysiological insights, enhance personalized education for K-12 students and adult learners. This study emphasizes the role of Electroencephalography (EEG), Functional Near-Infrared Spectroscopy (fNIRS), and other neurophysiological tools in assessing cognitive states and guiding A

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Rafiq, Hasan, Xiaohan Shi, Hengxu Zhang, Huimin Li, and Manesh Kumar Ochani. "A Deep Recurrent Neural Network for Non-Intrusive Load Monitoring Based on Multi-Feature Input Space and Post-Processing." Energies 13, no. 9 (2020): 2195. http://dx.doi.org/10.3390/en13092195.

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Non-intrusive load monitoring (NILM) is a process of estimating operational states and power consumption of individual appliances, which if implemented in real-time, can provide actionable feedback in terms of energy usage and personalized recommendations to consumers. Intelligent disaggregation algorithms such as deep neural networks can fulfill this objective if they possess high estimation accuracy and lowest generalization error. In order to achieve these two goals, this paper presents a disaggregation algorithm based on a deep recurrent neural network using multi-feature input space and p

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Li, Zhe, Aya Kanazuka, Atsushi Hojo, Yukihiro Nomura, and Toshiya Nakaguchi. "Multi-Modal Fusion Network with Multi-Head Self-Attention for Injection Training Evaluation in Medical Education." Electronics 13, no. 19 (2024): 3882. http://dx.doi.org/10.3390/electronics13193882.

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The COVID-19 pandemic has significantly disrupted traditional medical training, particularly in critical areas such as the injection process, which require expert supervision. To address the challenges posed by reduced face-to-face interactions, this study introduces a multi-modal fusion network designed to evaluate the timing and motion aspects of the injection training process in medical education. The proposed framework integrates 3D reconstructed data and 2D images of hand movements during the injection process. The 3D data are preprocessed and encoded by a Long Short-Term Memory (LSTM) ne

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Lin, Chin-Yi, Tzu-Liang (Bill) Tseng, and Tsung-Han Tsai. "A Multi-Machine and Multi-Modal Drift Detection (M2D2) Framework for Semiconductor Manufacturing." Applied Sciences 15, no. 12 (2025): 6500. https://doi.org/10.3390/app15126500.

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The semiconductor industry currently lacks a robust, holistic method for detecting parameter drifts in wide-bandgap (WBG) manufacturing, where conventional fault detection and classification (FDC) practices often rely on static thresholds or isolated data modalities. Such legacy approaches cannot fully capture the intricate, multi-modal shifts that either gradually erode product quality or trigger abrupt process disruptions. To surmount these challenges, we present M2D2 (Multi-Machine and Multi-Modal Drift Detection), an end-to-end framework that integrates data preprocessing, baseline modelin

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G.Ramachandra, Rao, Jayanth Kotte, Srilatha Bellamkonda, Soniya Bikki, and Hemanth Kumar Yenni. "Hybrid Surveillance for Abnormal Behaviour Detection with TST and Semantic AI." International Journal for Modern Trends in Science and Technology 11, no. 04 (2025): 377–82. https://doi.org/10.5281/zenodo.15172061.

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<em>The "Hybrid Surveillance for Abnormal Behaviour Detection with TST and Semantic AI" proposes an advanced framework designed to enhance college safety by detecting abnormal behaviours in surveillance videos through a synergistic integration of deep learning and semantic analysis. The system employs a Temporal Segment Transformers (TST) model, meticulously trained on the UCF-Crime dataset comprising 111,308 images organized into 37,098 temporal segment groups, to analyse video frames grouped into three distinct segments (early, middle, late). Utilizing a pre-trained Vision Transformer (ViT)

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Singh Samuel, Mr F. Richard, and M. Dhana Sakthi. "Predictive Framework for Water Quality Using Machine Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–7. https://doi.org/10.55041/ijsrem43834.

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Water quality is essential for human health and ecosystem stability, as pollution can cause serious health issues and harm wildlife. Large-scale and on-going monitoring is difficult using traditional methods of water quality assessment since they are frequently costly, time-consuming, and labour-intensive. This paper suggests a prediction framework that uses machine learning approaches to effectively assess water potability in order to get beyond these restrictions. To assess whether water is safe to drink, the system looks at important water quality factors such pH, organic carbon, chloramine

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Mrs. M. S. S. Lakshmi Lavanya, Mrs. G. Shruthi, M. Anusha, G. Anil, Aishwarya, and N. Vishnu Vardhan. "Utilizing Deep Learning for Drug Side Effect Insights: The Deep Side Framework." International Research Journal on Advanced Engineering Hub (IRJAEH) 3, no. 04 (2025): 1187–93. https://doi.org/10.47392/irjaeh.2025.0170.

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Drug side effect prediction is important for patient safety and drug development. Conventional approaches, such as clinical trials and post-marketing surveillance, are time-consuming, resource-intensive, and usually insufficient in detecting rare or delayed adverse effects. Current computational models, e.g., statistical and rule-based methods, are not well-suited to handle complex, large-scale biomedical data, which restricts their predictive power. To overcome such challenges, we present Deepside, a framework that is powered by artificial intelligence and utilizes deep learning as well as ma

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Ali, Arshad, Akash Akash, Anuj Kumar, Bilal Arshad, and Dr Anum Kamal. "DEEP LEARNING-BASED SENTIMENT ANALYSIS USING A HYBRID BERT AND BI-LSTM MODEL." Journal of Dynamics and Control 9, no. 5 (2025): 287–300. https://doi.org/10.71058/jodac.v9i5023.

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In the digital world, large amounts of content—ranging from social site posts and product response to news articles—contain valuable sentiments data that, if properly analysed, can provide significant insights for businesses, governments, and individuals. Sentiment Analysis, also known as opinion mining. Sentiment analysis mainly involves to have a good look on their thoughts, ideas, behaviours, opinions, sentiments. Traditionally we have used machine learning things to do the job but these models could not able to get what exactly we tries to achieve so we further extended our research to try

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Eckardt, Jan-Niklas, Christoph Rollig, Michael Kramer, et al. "Prediction of Complete Remission and Survival in Acute Myeloid Leukemia Using Supervised Machine Learning." Blood 138, Supplement 1 (2021): 108. http://dx.doi.org/10.1182/blood-2021-149582.

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Abstract Achievement of complete remission (CR) signifies a crucial milestone in the therapy of acute myeloid leukemia (AML) while refractory disease is associated with dismal outcomes. Hence, accurately identifying patients at risk is essential to tailor treatment concepts individually to disease biology. Machine Learning (ML) is a branch of computer science that can process large data sets for a plethora of purposes. The underlying mechanism does not necessarily begin with a manually drafted hypothesis model. Rather the ML algorithms can detect patterns in pre-processed data and derive abstr

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