AI-Powered Early Detection and Prevention System for Student Dropout Risk

International Journal of Computational and Experimental Science and Engineering, Год журнала: 2025, Номер 11(1)

Опубликована: Янв. 3, 2025

The increasing rate of student dropouts is a significant challenge in education systems worldwide, affecting both academic progress and institutional sustainability. This research presents an AI-driven predictive model aimed at early detection prevention dropouts. Leveraging advanced machine learning algorithms, including ensemble deep techniques, the analyzes variety data such as performance, attendance, behavioral patterns, socio-economic factors, psychological well-being. By identifying warning signs potential dropouts, provides actionable insights for educators administrators to intervene promptly. Additionally, system integrates personalized recommendations targeted support, ensuring students receive necessary resources improve their engagement performance. approach not only helps reducing dropout rates but also contributes fostering more supportive environment. Experimental results demonstrate effectiveness model, achieving high accuracy prediction offering promising implications its adoption educational institutions

Язык: Английский

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Towards Smarter E-Learning: Real-Time Analytics and Machine Learning for Personalized Education

International Journal of Computational and Experimental Science and Engineering, Год журнала: 2025, Номер 11(1)

Опубликована: Янв. 2, 2025

E-Learning platforms change fast, and real-time behavioural analytics with machine learning provides the most powerful means to enhance learner outcomes. The datasets undergo preprocessing techniques like Z-score outlier detection, Min-Max scaling for feature normalization, Ridge-RFE (Ridge regression Recursive Feature Elimination) selection in order improve accuracy reliability of predictions. Applying Gradient Boosting Machine, classification up a 94% level respect model about predictions on outcomes was achievable. Thus, applying this, feedback systems may offer timely recommendations or directions class that propel students toward better understanding how raise participation success percentages. However, this approach has some potential benefits but there are still various challenges such as managing data imbalance models generalize dynamic environment. Though hybrid methods mitigate problem, pipelines behaviour incorporation call significant computer-intensive resources infrastructure. This integration very high paybacks. It makes possible more responsive individual needs almost met manners, thus giving instantaneous feedback, content suggestions, interventions. Finally, convergence ML culminates adaptive environments which student engagement, retention, quality academic results.

Язык: Английский

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Predicting Media Impact: A Machine Learning Framework for Optimizing Corporate Communication Strategies in Architectural Practices

International Journal of Computational and Experimental Science and Engineering, Год журнала: 2025, Номер 11(1)

Опубликована: Фев. 8, 2025

The research investigates the role of media relations and corporate communications strategies architectural firms that conventionally pursue PR methodologies data-driven approaches have evolved. This has led to conduct studies use qualitative insights coupled with predictive modelling. These are used examine how companies evolving their approach in digital age. study ten leading architecture firms, assessing communication effectiveness through interviews, content analysis, social metrics. further predicts stakeholder engagement impact by applying machine learning models- Random Forest LSTM networks an accuracy 85%. Key findings include drivers based on sentiment, share ability, timing significant. demonstrated can drive strategic decision-making, optimize public relations, improve engagement. Moreover, provides easily scalable framework for forecasting purposes different markets. Further, it shows promise AI-driven strategies. Combining theory advanced analytics, this benefit from increasingly nature relations. been a major need proactive reputation management distribution. It enables others better adapt changing waves response maximal positive

Язык: Английский

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Electronic Components Detection Using Various Deep Learning Based Neural Network Models

International Journal of Computational and Experimental Science and Engineering, Год журнала: 2025, Номер 11(1)

Опубликована: Янв. 22, 2025

Electronic components of different sizes and types can be used in microelectronics, nanoelectronics, medical electronics, optoelectronics. For this reason, accurate detection all electronic such as transistors, capacitors, resistors, light-emitting diodes chips is great importance. purpose, study, an open source dataset was for the five components. In order to increase amount dataset, firstly, data augmentation processes were performed by rotating component images at certain angles right left directions. After these processes, multi-class classifications using deep learning based neural network models, namely Vision Transformer, MobileNetV2, EfficientNet, Swin Transformer Data-efficient Image Transformer. As a result with various necessary evaluation metrics precision, recall, f1-score accuracy obtained each model, highest value 0.992 model.

Язык: Английский

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A Context-Aware Content Recommendation Engine for Personalized Learning using Hybrid Reinforcement Learning Technique

International Journal of Computational and Experimental Science and Engineering, Год журнала: 2025, Номер 11(1)

Опубликована: Фев. 5, 2025

In the evolving landscape of e-learning, delivering personalized content that aligns with learners' needs and preferences is crucial. This study proposes a Context-Aware Content Recommendation Engine (CACRE) utilizes Hybrid Reinforcement Learning (HRL) technique to optimize learning experiences. The engine incorporates contextual data, such as pace, preferences, performance, deliver tailored recommendations. proposed HRL model combines Deep Q-Learning for dynamic selection Policy Gradient Methods adapt individual trajectories. Experimental results demonstrate significant improvements in learner engagement, relevance, knowledge retention. approach underscores potential context-aware recommendation systems revolutionize education by fostering adaptive interactive environments.

Язык: Английский

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Data-Driven Insights: A Critical Analysis of Farmer Call Centre Data Using Machine Learning Techniques

International Journal of Computational and Experimental Science and Engineering, Год журнала: 2025, Номер 11(2)

Опубликована: Март 23, 2025

The agricultural sector plays a crucial role in India's economy, society, and environment. Agriculture is the primary source of livelihood for significant portion Indian population, employing over half country's workforce. It contributes substantially to Gross Domestic Product (GDP) remains vital rural development poverty alleviation. Experts use different kinds smart systems figure out problems on farms find possible solutions. help experts collect analyze information regarding issues farmers meet. This study aimed investigate query data from Kisan Call Centers (KCCs) 2020 2023 identify key issues, understand farmers' challenges, provide data-driven policy program insights. Python was used processing, Power BI visualization, Machine learning algorithms Natural Language Processing libraries analysis

Язык: Английский

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A Graph-Based and Pattern Classification Approach for Kannada Handwritten Text Recognition Under Struck-Out Conditions

International Journal of Computational and Experimental Science and Engineering, Год журнала: 2025, Номер 11(1)

Опубликована: Март 4, 2025

This research focuses on the processing and identification of handwritten Kannada text, particularly under struck-out conditions. The database considered in this study comprises data. When such a is processed using optical character recognition (OCR)-based digital systems, output may often be an unrecognizable format. To address issue, model has been developed incorporating pattern classification graph-based method for text identification. For classification, feature extraction performed two different classes with support vector machines (SVMs) classifier. In approach, strokes are analyzed shortest path algorithm. handle zigzag or wavy all possible paths strike-out identified, suitable features extracted further processing. synthesized/recovered inpainting cleaning to ensure recovery. proposed methodology tested both trained untrained datasets script. Performance evaluation was conducted three parameters: precision, F1 score, accuracy.

Язык: Английский

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AI as a Cognitive Partner: A Systematic Review of the Influence of AI on Metacognition and Self-Reflection in Critical Thinking

Опубликована: Март 28, 2025

Educational settings today include Artificial Intelligence (AI) systems that transform student interaction with critical thinking and metacognitive processes. The research assesses AI’s positive negative effects on developing cognitive abilities through systematic analysis review. Contemporary learning tools backed by artificial intelligence provide individualised feedback, automated tutoring, adaptive testing enhances students’ problem-solving skills awareness. Concerns regarding offloading, sloth, algorithmic bias challenge the possible impact of AI independent autonomy. This study synthesises existing to investigate how works as a partner supports ability potential barrier long-term engagement in environments. Evidence shows assistance self-regulation development, but overdependence it results lower decreased thinking. Data privacy issues, access fairness concerns, decision-making biases make necessary for educational institutions control their incorporation technologies carefully. review highlights teaching practices ethical use advocates equitable AI-human collaboration produce compelling experiences. report recommends educators policymakers implement measures ensure applications augment capabilities rather than replace them. Long- term studies must assess resilience they learn strategies. aims construct AI-fortified designs leveraging risks enhance inquiry skills, self-reflection, capabilities.

Язык: Английский

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Automating Compliance In Devops Pipelines

International Journal of Computational and Experimental Science and Engineering, Год журнала: 2025, Номер 11(2)

Опубликована: Апрель 9, 2025

The expanding popularity of DevOps techniques revolutionized the software delivery pipelines through quick efficient code deployment methods. research Field automated compliance detection within workflows has become essential for solving this problem. This develops a new conceptual model which ensures regulatory criteria flow naturally throughout every stage pipelines. approach performs detailed theoretical evaluation reveals multiple potential benefits including prompt miscon figuration_errors identification as well standard policy enforcement cloud settings and better conditions developers. We identify two forthcoming enhancements methodology comprise artificial intelligence systems development along with multi-cloud network connectivity capabilities. Our proposal delivers blueprint upcoming experimental testing although we prioritize uncovering unified architecture instead practical implementation. analyzes modern industry while establishing strategic strategy to place functions directly results in security risk reduction accelerated compliant solutions. helps communities practitioners reframe into an integrated dynamic factor current practices develop more dependable systems. Organizations achieve by integrating their pipeline

Язык: Английский

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Artificial Intelligence-Based color Reconstruction of Mogao Grottoes Murals Using Computer Vision Techniques

International Journal of Computational and Experimental Science and Engineering, Год журнала: 2025, Номер 11(2)

Опубликована: Апрель 9, 2025

The Mogao Grottoes murals have deteriorated over centuries due to environmental exposure, pigment degradation, and natural ageing, making cultural heritage preservation difficult. AI computer vision can identify, classify, reconstruct faded pigments, revolutionizing color restoration. This reconstructs mural sections using deep learning, image processing, data implemented through TensorFlow, PyTorch OpenCV. study uses high-resolution Digital Dunhuang database images of 50 pigments categorized by color, stability, chemical composition. CNNs learning-based mapping algorithms detect fading suggest restorations pigments. reconstructions along with history accuracy expert evaluations records. Artificial intelligence-driven conservation detects precisely missing sections, matches restored colors historical authenticity, improving accuracy, efficiency, scalability. Scientifically, AI-based digital outperforms manual preserves faithfully sites artworks global learning-driven restoration models. first reproducible scientific model (CNN, GAN algorithms) analysis in was created.

Язык: Английский

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