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Detecting and Predicting Learner's Dropout Using KNN Algorithm

Detecting and Predicting Learner's Dropout Using KNN Algorit...

2024 OPJU International technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Guru Priya, V. Eliyas, Sherin Sathish Kumar, M. Hindustan Institute of Technology and Science Department of Computer Applications Chennai India

ISBN: (纸本)9798350373783

Predicting student dropout is crucial for early intervention and support, addressing a significant loss of potential human capital in the education system. This paper presents a machine-learning approach to predict student dropout using a dataset of student records. Our model leverages various features including demographic information, academic performance, attendance records, and fee payment details to estimate the probability of dropout. A survey conducted among college students in Tamil Nadu sheds light on reasons for discontinuing courses, emphasizing the multifaceted nature of the dropout problem. The proposed predictive model will be evaluated using a large dataset, comparing its effectiveness and accuracy with existing models. This research aims to facilitate proactive interventions by educational institutions, leveraging technology and data analytics to improve retention rates amidst the challenges posed by the pandemic and beyond. Student dropout prediction is paramount for early intervention in the education system, mitigating the loss of potential human capital. This study employs a machine-learning approach to forecast student dropout utilizing a comprehensive dataset comprising demographic information, academic performance, attendance records, and fee payment details. A survey conducted among college students across Tamil Nadu highlights the multifaceted nature of dropout, encompassing socioeconomic and psychological factors. The proposed predictive model will undergo rigorous evaluation against existing benchmarks to assess its efficacy and accuracy. By enabling proactive interventions, this research endeavors to enhance retention rates through the integration of technology and data analytics, particularly in the wake of the pandemic, shaping the future of education. © 2024 IEEE.

关键词： Students

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Immune-Based Nutrition Deficiency Analysis Using Classification Algorithms 2

Immune-Based Nutrition Deficiency Analysis Using Classificat...

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2nd International Conference on Data Science and Information System, ICDSIS 2024

作者： Nishaanth, K. Rajasekar, M. Hindustan Institute Of Technology And Science Department Of Computer Applications Chennai India

ISBN: (纸本)9798350381665

Nutrition deficiency poses a significant global health issue, impacting immune functionality and overall wellness. Recognizing the intricate nexus between nutrition and immune well-being stands crucial for devising efficacious intervention tactics. This investigation delves into utilizing classification algorithms to scrutinize deficiencies in nutrition with immune ramifications. Employing feature engineering strategies, it extracts pertinent features, encompassing counts of White Blood Cells, Neutrophils, Lymphocytes, and Immune cells. According to the Immunity, this categorizes the dataset. Various classification algorithms, including Random Forest, Decision Tree, and Naive Bayes, are employed to construct predictive models. The efficacy of each model is gauged using performance metrics such as Accuracy, Precision, Recall, and F1-score. By comparing the model's performance, the Random Forest algorithm provides better accuracy with a rate of 99.4. Thus, utilizing this, it can offer suggestions for nutritional content consumption based on an individual's immune status. © 2024 IEEE.

关键词： Decision trees

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Optimization Strategies in Deep Learning Method ADO for Fake News Detection on Social Media 8

Optimization Strategies in Deep Learning Method ADO for Fake...

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8th International Artificial Intelligence and Data Processing Symposium, IDAP 2024

作者： Steni Mol, T.S. Gokila, S. Hindustan Institute of Technology and Science Department of Computer Applications Chennai India

ISBN: (纸本)9798331531492

The emergence of social media as a primary platform for news consumption has created a complex landscape, marked by accessibility and rapid dissemination alongside the proliferation of misinformation. This dual nature underscores the pivotal necessity for effective strategies in detecting false information to mitigate its detrimental impacts on society. In response to this critical need, this paper introduces a novel methodology, CNN-LSTM-ADO, which amalgamates Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Dingo Optimization (DOX) and Adam Optimization. Here, we introduce the CNN-LSTM-ADO framework tailored for identifying fake news. This framework utilizes the Dingo optimizer (DOX) and Adam optimizer to train the CNN-LSTM model. By consolidating complementary strengths of these techniques, CNN-LSTM-ADO endeavours to augment the precision and efficiency of fake news detection across social media platforms © 2024 IEEE.

关键词： Convolutional neural networks

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Helmet Detection System Using Yolo V2

Helmet Detection System Using Yolo V2

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2024 IEEE International Conference on Information technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Arun, M.D.R. Jaitly, Vanita Hindustan Institute of Technology and Science Department of Computer Applications Chennai India

ISBN: (纸本)9798350382693

The risk of head injuries. The methodology The increasing concern for safety in a variety of work and leisure environments mandates the production of robust and efficient helmet detection systems. This study offers a fresh method for helmet detection, utilizing the (You Only Look Once version 2) YOLOv2 algorithm. The proposed system leverages the capabilities of YOLOv2 for real-time object detection, providing a proactive solution to enforce helmet-wearing regulations and mitigate impacts that encompass gathering of data, pre-processing, designing of the model architecture, training, and assessment. Results from experiments show the YOLO has achieved the greatest mAP success rate of 96.72%. How well the suggested approach works in precisely detecting helmets in diverse environments and scenarios. Furthermore, discussions on implementation considerations, challenges, and future research directions are provided, highlighting the potential impact of the helmet detection system on enhancing safety across industries and activities. © 2024 IEEE.

关键词： Deep learning

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DAT-Net: Filling of missing temperature values of meteorological stations by data augmentation attention neural network 4

DAT-Net: Filling of missing temperature values of meteorolog...

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2024 4th International Conference on Artificial Intelligence and Industrial technology applications, AIITA 2024

作者： Guo, Xinshuai Hou, Tianrui Wu, Li Department of Computer Technology and Applications Qinghai University Xining Qinghai China

For a long time, filling in the missing temperature data from meteorological stations has been crucial for researchers in analyzing climate variation cases. In previous studies, people have attempted to solve this problem by using interpolation and deep learning methods. Through extensive case studies, it is observed that the data utilization rate of convolutional neural networks based on PConv is low at a high missing rate, which will result in the poor filling performance of each model at a high missing rate. To solve these problems, a Data Augmentation Attention Neural Network (DAT-Net) is presented. DAT Net uses encoder and decoder structures, which include a data augmentation training mechanism (DAM) to enhance model training. In addition, a time encoder (TED) has been developed to assist the model in learning the temporal dependencies of the data. To evaluate DAT-Net, 75% and 85% of experiments were performed, while comparisons were made with Linear, NLinear, DLinear, PatchTST, and GSTA-Net. The results showed that when the missing rate was 75%, DAT-Net decreased by 55.22%, 55.05%, 55.18%, 28.73%, and 12.35% on MAE and 54.08%, 53.88%, 54.08%, 35.48% and, 14.51% on RMSE, R2 increased by 3.80%, 3,75%, 3.68%, 0.55%, and 0.27%, respectively. © Published under licence by IOP Publishing Ltd.

关键词： Convolutional neural networks

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Ten Years of Research on Cardiac Imaging in Diagnosing Heart Diseases 15

Ten Years of Research on Cardiac Imaging in Diagnosing Heart...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Chaithra, C.S. Siddesha, S. Niranjan, S.K. Department of Computer Applications JSS Science and Technology University Mysuru India

ISBN: (纸本)9798331300579

Cardiac images play a vital role in early detection and diagnosing the heart diseases. Due to the advancement in machine learning techniques many tools are emerging for early detection of heart disease and its diagnosis. Image based diagnosis requires previous clinical data which is extracted from hidden image patterns. Machine learning algorithms with cardiac images gaining prominence in healthcare by automating calculations and finding new patterns in data and provide accurate results for diagnosing patients in early stages. This work provides the detailed 10 years of studies on different cardiac images for diagnosing the heart diseases using various machine learning methods. © Grenze Scientific Society, 2024.

关键词： Magnetic resonance imaging

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Exploring SPARQL query types to improve Ontology Mapping and Retrieval Augmented Modelling for auto-generated questions

Exploring SPARQL query types to improve Ontology Mapping and...

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2024 Joint Ontology Workshops - Episode X: The Tukker Zomer of Ontology, and Satellite Events, JOWO 2024

作者： Tharaniya Sairaj, R. Balasundaram, S.R. Department of Computer Applications National Institute of Technology Tamilnadu Tiruchirappalli India

The use of SPARQL is evergreen for querying, extracting and expanding named entities from RDF datasets, significantly enhancing text processing applications such as Automatic Question Generation (AQG). Recent research employs pretrained Large Language Models (LLMs) for AQG to reduce manual costs, but these models are limited by their dependence on training data. This in turn makes LLMs to conterfeit the answer-revealingness challenge in automatic Multi-hop Question Generation. This challenge occurs in the process of Multi-hop Question Generation as it requires integration of named entities from multiple sources and deep comprehension of these interconnected concepts, which is quite challenging. In this context, Retrieval Augmented Models (RAM) have gained attention in NLP for improving text processing through enhanced information extraction, yet their application in AQG is limited. This research addresses this gap by RAM's workflow with attention to its first phase - Input Text Enrichment via Named Entity Expansion—is crucial for generating diverse, comprehensive questions. But, Effective named entity expansion is facilitated by ontology mapping to align entities to various ontologies, which is more demanding. To address this requirement, SPARQL querying techniques such as multi-querying, step-back querying, and sub-querying are examined to enhance named entity expansion accuracy, thereby improving RAM's efficacy, leading to well-formed auto-generated questions. © 2024 Copyright for this paper by its authors.

关键词： Structured Query Language

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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 2025年第18期84卷 18733-18780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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Synergistic Solutions for Cloud Cybersecurity and Financial Operations using AI, Blockchain and Quantum Computing

Synergistic Solutions for Cloud Cybersecurity and Financial ...

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2024 International Conference on Cybernation and Computation, CYBERCOM 2024

作者： Boopalan, Sunil SRM Institute of Science and Technology Department of Computer Applications Chennai India

ISBN: (纸本)9798331542344

This research paper further builds on previous research by proposing a combination of AI and blockchain in the context of integration of quantum computing to improve the cybersecurity of cloud and financial operations, especially for India's UPI and digital rupee platforms. This is especially the case when digital transactions are on the rise and their security against rampant cyber threats and overall soundness of operations must be guaranteed. The following is a novel framework that embellishes AI, blockchain, and quantum computing to meet these challenges outlined in this paper. Machine learning and pattern recognition will be implemented to scope out and prevent security threats, while blockchain is to ensure a secure and open system of transactions;quantum computing will help to provide extraordinary features for data security and data encryption. The research also seeks to examine the current implementation approaches, advantages, and disbursement issues of this integration and subsequently makes recommendations that can help policymakers and practitioners make improvements for the improvement of trust and security in the implementation of financial solutions on the cloud. © 2024 IEEE.

关键词： Cybersecurity

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Identifying Biomarkers for Papillary Thyroid Carcinoma Using Machine Learning

Identifying Biomarkers for Papillary Thyroid Carcinoma Using...

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2024 International Conference on Advances in Modern Age Technologies for Health and Engineering Science, AMATHE 2024

作者： Geetanjali, R. Geetha, D. Evangelin Ramaiah Institute Of Technology Department Of Master Of Computer Applications Bangalore India

ISBN: (纸本)9798350371567

This study aimed to identify potential biomarkers and gain insight into the underlying biological processes of papillary thyroid carcinoma (PTC), the most common form of thyroid cancer, using the GSE27155 dataset from the NCBI GEO database. By analyzing the data with three machine learning algorithms, the researchers identified 137 differentially expressed genes and several pathways associated with PTC. These genes and pathways may serve as targets for novel therapies for PTC and provide clinicians with potential biomarkers for early diagnosis and treatment. By examining the expression levels of these genes in patient samples, clinicians can identify patients at risk of developing PTC, leading to earlier diagnosis and treatment. The identified genes and pathways can also serve as potential targets for novel therapies for PTC. By targeting the biological pathways underlying PTC, clinicians can develop new treatments that are more effective and have fewer side effects than traditional treatments. Overall, the study highlights the importance of using machine learning to identify differentially expressed genes and potential biomarkers for PTC. By gaining a deeper understanding of the biological processes underlying PTC, clinicians can develop more accurate diagnostic tools and more effective treatments, ultimately improving patient outcomes. © 2024 IEEE.

关键词： Biomarkers

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