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Learner’s intention analysis to mitigate the cold start problem in personalized learning recommendation systems

Multimedia Tools and applications 2024年 1-55页

作者： Pal, Saurabh Pramanik, Pijush Kanti Dutta Choudhury, Prasenjit Department of Computer Science and Engineering School of Engineering Brainware University Kolkata India School of Computer Applications and Technology Galgotias University Uttar Pradesh Greater Noida India Department of Computer Science & amp Engineering National Institute of Technology Durgapur West Bengal India

Understanding the learner’s requirements and status is important for recommending relevant and appropriate learning materials to the learner in personalized learning. For this purpose, the learning recommendation system must include the learner’s contextual and behavioral information. However, in the case of cold start problems, the system has no prior information about a new user. Moreover, the system cannot acquire the required information instantly because the automated and instantaneous acquisition of this information is not trivial. This hinders the personalized recommendation for a new learner. Engaging the learner in a questionnaire session may allow the learning system to acquire most of the required information. However, this makes the learner uninterested and disengaged with the system. To address this, we propose a recommendation system for personalized learning that mitigates the cold start problem by acquiring important information by analyzing the learner’s questions submitted to the learning system. By analyzing the meta-information of a question, we identified the learner’s exact requirements, domain and topic knowledge, and expected knowledge gain from the learning material. Ontology, BiLSTM, and context mapping are used for learner modelling and topic classification, and identifying suitable learning materials, respectively. Furthermore, MCDM techniques are used for ranking the learning materials. Our experimental assessment and validation suggest that the information acquired based on our proposed approach is sufficient for understanding a new learner’s needs and suitability and, accordingly, for recommending suitable personalized learning materials. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Recommender systems

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Image Classification of Brain Tumors through Hybrid Learning 4

Image Classification of Brain Tumors through Hybrid Learning

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4th International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Sreeja, S.P. Asha, V. Saju, Binju Suchitra Dwivedi, K.M. Kairoon, P. Bhavana, K. New Horizon College of Engineering Master of Computer Applications Bengaluru India

ISBN: (纸本)9798350343670

Brain tumor, the well-known deadly medical condition where treatment sometimes fails to show results. Various treatments and medical diagnosis shows ineffective as the chances of this condition appearing again are more than curing them in first attempt. The abnormal growth of cells inside or surrounding brain causes these brain tumors which may differ depending on the type of tumor in each individual. The study proposes a hybrid model to classify the brain images. First the images are pre-processed with the help of Image resizing, Gray scale conversion and noise removal with the help of combined Weiner filter and Median filter. Then the Gaussian Mixture based histogram equalization are used to enhance the images. A Dense Convolutional neural network is used for extracting the Features of the images. An optimized ResNet101 model with improved Aquila optimization is used for classifying the brain tumor images. Performance of the model is compared with other studies in literature. The proposed model has obtained an accuracy of 99.01%, along with the various performance metrics as sensitivity of 98.60%, specificity of 98.01%, precision of 98%, F1 score of 98.44%. © 2024 IEEE.

关键词： Brain Tumor DCN Gaussian Mixture IAO ResNet

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Aggregated Relative Similarity (ARS): a novel similarity measure for improved personalised learning recommendation using hybrid filtering approach

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Multimedia Tools and applications 2024年第18期84卷 1-48页

作者： Pal, Saurabh Dutta Pramanik, Pijush Kanti Choudhury, Prasenjit Department of Computer Science and Engineering National Institute of Technology Durgapur West Bengal Durgapur India School of Computer Applications and Technology Galgotias University Uttar Pradesh Greater Noida India Department of Computer Science and Engineering School of Engineering Brainware University Kolkata India

To improve the effectiveness of online learning, the learning materials recommendation is required to be personalised to the learner material recommendations must be personalized to learners. The existing approaches are ineffective in recommending learning materials according to the learner’s learning situation, the learner’s internal learning characteristics, and fitting to the learner’s preferences and learning suitability. In this work, we propose a hybrid recommendation approach cthat combines the advantages of knowledge-based and collaborative filtering approaches. The knowledge-based filtering approach enables us to find the most suitable learning materials that fit the learner’s situation and learning state by appropriately mapping the learners’ contexts with the metadata of the learning materials. We use collaborative filtering to refine learning material selection by considering learner-learner similarity. We propose a novel similarity measure, aggregated relative similarity (ARS), to determine similar learners based on implicit learner characteristics such as education, knowledge, cognitive ability, and learning style. The experimental results show that the proposed ARS algorithm outperforms popular similarity measures such as Jaccard similarity, Sorensen-Dice coefficient, and overlap coefficient. The overall recommendation performance of the proposed hybrid approach attained mean absolute error (MAE) and root mean square error (RMSE) of 0.681 and 0.9198, respectively. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Collaborative filtering

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Quick Verification and Security Integrity of Emergency Messages Communication over Wireless Network 3

Quick Verification and Security Integrity of Emergency Messa...

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3rd International Conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Singh, Dhananjay Kumar Bhardwaj, Diwakar Gla Univerisity Department of Computer Engineering and Applications UP Mathura India

ISBN: (纸本)9798331528201

Communication over wireless networks is increasingly vital for reliable, low-latency transmission of small messages by resource-constrained devices. These devices often struggle with the demands of implementing robust integrity security for emergency messages. Our solution, the BB-MAC (Bit by Bit Message Authentication Code) method, addresses this challenge with a quick, memory-efficient approach rooted in the Carter-Wegman structure. The key idea is to shift resource-intensive computations to idle periods, conserving critical time during latency-sensitive phases when new data must be processed. BB-MAC employs a universal hash function for Bit by Bit pre-computation of integrity security, minimizing XOR cycles during critical operations. Our research on embedded systems shows that BB-MAC outperforms recent methods in terms of delay and memory overhead, particularly for small payloads, making it a superior choice for safeguarding resource-limited devices under stringent security and latency requirements. © 2024 IEEE.

关键词： Authentication Protocol

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Prediction of Customer Purchasing Patterns for Retail Optimization Using Market Basket Techniques 8

Prediction of Customer Purchasing Patterns for Retail Optimi...

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8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Farheen, Zaiba Dharani, Andhe Rv College of Engineering Department of Master of Computer Applications Bengaluru India

ISBN: (纸本)9798331505462

This paper addresses the critical task of assessing customer purchasing behaviour in retail, which directly impacts marketing strategies and inventory management. The paper presents a data processing and analysis pipeline specifically designed for market basket analysis and association rule mining. Utilizing the Apriori algorithm, our system identifies frequent itemsets and association rules, revealing key insights into purchasing patterns. The pipeline employs AWS S3 for data storage, Apache Airflow for ETL processes, and SQLite3 for data analysis, with visualizations provided through Power BI dashboards. The proposed model effectively identifies complex product relationships and predicts customer behaviour, outperforming traditional methods. The integration of a Large Language Model (LLM) like GPT 3.5-Turbo for customer feedback processing enhances the insights derived. This study enhances marketing strategies, inventory management, and customer satisfaction by offering actionable recommendations for product bundling and cross-selling. The findings highlight the significant potential of advanced data processing and machine learning algorithms to transform retail analytics, providing businesses with reliable tools for informed decisionmaking. Notably, the strongest association rule identified was between Wine and Frozen Meals, as well as Canned Vegetables and Beer, with a Support score of 3.6%, Confidence of 50.7%, and Lift value of 3.04. Furthermore, it was observed that the accuracy of Customer Feedback Analysis using GPT 3.5-Turbo was 92% while the traditional method of using in-built python libraries(Naive Bayes Classifier) gave an accuracy of only 84%. © 2024 IEEE.

关键词： Beer

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NeuroNet: A Deep Learning Framework for Stroke Analysis 2

NeuroNet: A Deep Learning Framework for Stroke Analysis

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Prasad, Arpana Asha, V. Tressa, Neethu Sharma, Divya Deepthi, V. Divyashree, S. New Horizon College of Engineering Master of Computer Applications Bengaluru India

ISBN: (纸本)9798350327533

The revolutionary potential of a deep learning framework for stroke analysis and prediction in the healthcare industry is examined in this paper. The suggested framework uses large datasets, such as patient records, medical images, and pertinent health information, to create histicated neural networks in order to address the critical need for early detection and intervention in stroke cases. Important areas like stroke type classification, natural language processing, image analysis, treatment optimization, outcome prediction, telemedicine, and explainable artificial intelligence are highlighted in the thorough review of related work. The steps involved in data collection, preprocessing, model selection, and result interpretation are all carefully laid out in the methodology. A thorough evaluation of machine learning models, such as Random Forest, SVM, XGBoost, and Logistic Regression, is carried out;Random Forest continuously performs well. The results highlight how difficult it is to predict strokes and how crucial it is to choose models carefully. With the ultimate goal of improving stroke diagnosis, treatment, and prevention for improved patient care through ongoing research and technological advancements, ethical considerations and adherence to healthcare regulations in AI applications are emphasized. © 2024 IEEE.

关键词： and Data Analysis Artificial Intelligence Risk Prediction Stroke

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Generative AI Explored: Bridging Theory and Practice with Insights from DocQA Implementation 8

Generative AI Explored: Bridging Theory and Practice with In...

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8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Singh, Ritika Dharani, Andhe Rv College of Engineering Department of Master of Computer Applications Bengaluru India

ISBN: (纸本)9798331505462

Generative AI, a field encompassing models that generate text, images, and various other forms of media, is explored in this work, with a focus on its practical applications through DocQA solutions. This technology has a significant transformation on various industries by offering innovative methods to tackle complex challenges. The discussion provides a detailed overview of Gen AI, including its mechanisms, capabilities, and limitations. It then delves into a case study of DocQA, which is designed to enhance document processing and question answering tasks. By examining real-world applications and outcomes, this work reveals the practical benefits, challenges, and best practices for using Gen AI in document centric workflows. The insights aim to guide practitioners and researchers in effectively integrating Gen AI into their processes, enhancing efficiency and accuracy in managing information. © 2024 IEEE.

关键词： Generative adversarial networks

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Automated Loan Approval System using Machine Learning 4

Automated Loan Approval System using Machine Learning

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Srivastava, Swati Gla University Department of Computer Engineering and Applications UP Mathura India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529635

In today's world, it's common for people to apply loans from banks and financial institutions for various reasons. But not everyone who applies can be approved. We often hear about cases where individuals fail to repay their loans, causing significant losses for banks. Making decisions about loan approvals is risky and challenging. Previous works considered single algorithm to make predictions where accuracy is low. To enhance the accuracy is a challenge. So, the goal of this study is to use multiple machine learning algorithms to extract key information about the loan data which could help organizations make better decisions about approving or rejecting loan requests from customers. In this paper, we analyze real bank credit data and apply various machine learning algorithms to determine the creditworthiness of customers. The accuracies of 56.28%, 59.46%, 57.34%, 63.23%, and 73.79% are obtained for logistic regression, support vector machine, Decision tree classifier, Random Forest Classifier, and ensemble method respectively. The aim of this work is to train a machine learning model to predict the eligibility for the applied loan and to create an interface for different types of loans. © 2024 IEEE.

关键词： Adversarial machine learning

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Classification of Lung Cancer Images Using Optimized Hybrid Deep Learning Model 2

Classification of Lung Cancer Images Using Optimized Hybrid ...

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Sreeja, S.P. Asha, V. Saju, Binju Harshitha, E.S. Achuth, H. Sengupta, Kritish New Horizon College of Engineering Master of Computer Applications Bengaluru India

ISBN: (纸本)9798350327533

This research study provides a concise overview of lung diseases, encompassing their diverse nature and impact on the respiratory system. Lung diseases can result from various factors, including genetics, environmental influences, and lifestyle choices. Diagnostic methods and treatment options are discussed, emphasizing the importance of early detection and a multifaceted approach to managing these conditions. Preventive measures, such as vaccination and smoking cessation, are very much vital in reducing lung diseases. This study serves as a valuable resource for those seeking a comprehensive understanding of these conditions and their management. The proposed model demonstrates impressive outcome with an accuracy level of 99.07%, a sensitivity level of 98.40%, a specificity level of 99.09%, a precision level of 98.11 %, and an Fl score of 97.94%. © 2024 IEEE.

关键词： DCNN Gaussian Mixture IAO Lung cancer ResNet

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Comparative Analysis of Linear Regression, Random Forest Regressor and LSTM for Stock Price Prediction 8

Comparative Analysis of Linear Regression, Random Forest Reg...

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8th IEEE International Conference on Computing, Communication, Control and Automation, ICCUBEA 2024

作者： Gorde, Preetish Sanjay Nilesh Borkar, Suchita Pimpri Chinchwad College of Engineering Computer Applications Department Pune India

ISBN: (纸本)9798350391770

Stock price prediction is an important topic of research in financial markets and many machine learning algorithms are used to estimate future stock prices. This study compares three prominent stock price prediction algorithms: Linear Regression, Random Forest, and Long Short-Term Memory (LSTM). The main objective of the research is to assess these algorithms' predictive ability using R-squared (R2), Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE) metrics. The study uses historical data from ten S&P 500 and ten NIFTY 50 stocks to represent a wide range of industries and market circumstances. This data is used to train and test each algorithm, and the evaluation criteria indicated above are used to determine each method's performance. The study's findings offer an insight into the ability of Linear Regression, Random Forest, and LSTM models in predicting stock values across markets and sectors. © 2024 IEEE.

关键词： Mean square error

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