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Adaptive E-Learning Environments: A Methodological Approach to Identifying and Integrating Multi-layered Learning Styles

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SN computer science 2024年第6期5卷 772页

作者： Ramesh, M. Jayashree, R. Department of Computer Science and Applications Faculty of Science and Humanities SRM Institute of Science and Technology Department of Computer Applications Faculty of Science and Humanities SRM Institute of Science and Technology

E-learning environments represent digital platforms designed to facilitate online learning experiences. Recognizing the diverse learning preferences of individuals, the need for identifying and integrating multi-layered learning styles within these environments is paramount. Existing approaches often face limitations in accurately capturing and accommodating these intricate learning styles. To address these challenges, this study proposes a novel approach. Firstly, the integration of word embedding-based feature extraction transforms textual data into continuous vector representations, enhancing feature robustness and meaningfulness. Secondly, leveraging deep belief networks (DBNs) allows for the automatic learning of hierarchical data representations, improving model performance by capturing complex patterns. Thirdly, the DBN-based model accommodates multi-layered learning styles, including visual, auditory, kinaesthetic, and read/write preferences, offering personalized recommendations to learners. Lastly, the proposed approach is scalable and generalizable, capable of handling large datasets and diverse educational contexts, thus enhancing the efficacy of e-learning platforms. This innovative approach demonstrates promising advancements in learning style identification within e-learning environments, providing personalized guidance to learners and improving the overall effectiveness of online education. The proposed Deep Belief Network (DBN) model exhibits a significant average increase in accuracy compared to other methods implemented in Python. With an accuracy of 99.5%, the DBN model surpasses the accuracy of the K-Nearest Neighbours (KNN) and Random Forest (RF) models by 25.4 and 3.3%, respectively. This substantial improvement underscores the superiority of the proposed DBN model in learning style identification. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep belief networks E-learning environments Learning style identification Multi-layered learning styles Word embedding

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Hybrid Deep Learning Model to Predict Students’ Sentiments in Higher Educational Institutions

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2025年第1期19卷 46-61页

作者： Ananthi, Claral Mary T. Department of Computer Applications Faculty of Science and Humanities SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chengalpattu India

Sentiment analysis has been widely used in various fields of social media, education, and business. Specifically, in the education domain, the usage of sentiment analysis is difficult due to the huge amount of information, the nature of language, and processing the diverse perceptions of students. Deep learning emerges as an advanced concept in the realm of machine learning that learns features automatically from raw text data, making them well-suited for sentiment analysis tasks. In recent years, deep learning has been used in analyzing the sentiments. Deep learning architectures have surpassed other machine learning paradigms for performing sentiment analysis. The ability to analyze automatically the students’ sentiments enables HEI to process huge amounts of unstructured data quickly, efficiently, and cost-effectively. The paper aims to predict the sentiments of students’ reviews posted in VLE regarding online learning that enables the educators to optimize their teaching methods for the best results. This study paper explores the usage of CNN, LSTM, and hybrid CNN-LSTM for the prediction of sentiments. The proposed hybrid CNN-LSTM architecture achieves superior performance compared to other baseline algorithms with respect to accuracy, precision, recall, and F1 score. According to outcomes, the recommended technique achieves remarkable accuracy of 97%. The findings facilitate the progress of a more efficient deep learning sentiment prediction system that gives valuable insights from a huge volume of students’ textual data. © 2025 by the authors of this article.

关键词： Contrastive Learning

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Hybrid CNN-Transformer for Aerial Object Detection: A Novel Architecture for Enhanced Detection Accuracy 2

Hybrid CNN-Transformer for Aerial Object Detection: A Novel ...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Patel, Sanskruti Smt. Chandaben Mohanbhai Patel Institute of Computer Applications Faculty of Computer Science and Applications CHARUSAT India

ISBN: (纸本)9798331505745

Aerial object detection is crucial for applications such as surveillance and environmental monitoring, but challenges such as small object size, complex backgrounds, and variations in scale hinder the performance of traditional models. This paper presents a novel hybrid architecture that combines Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) for enhanced aerial object detection. The model leverages CNNs for spatial feature extraction (using ResNet50) and transformers for global context understanding, aiming to improve both the precision and recall of the detection system. The proposed model is evaluated on the VisDrone dataset and compared with widely used object detection models, YOLOv3 and Faster R-CNN. The results show that the Hybrid CNN-Transformer model outperforms both YOLOv3 and Faster R-CNN, achieving a mAP of 0.45 at IoU 0.5 and 0.33 at IoU 0.75, along with higher Precision (0.58), Recall (0.52), and F1 Score (0.55). These results demonstrate the usefulness of the hybrid approach to avail the improvements in detection accuracy and a better trade-off between Precision and Recall, particularly for challenging aerial images with occlusions and scale variations © 2025 IEEE.

关键词： Convolutional neural networks

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Towards a Greener Future: Harnessing Green Computing to Reduce ICT Carbon Emissions 7

Towards a Greener Future: Harnessing Green Computing to Redu...

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Patel, Unnati A. Patel, Sanskruti Nanavati, Jay Faculty of Computer Science and Applications Smt. Chandaben Mohanbhai Patel Institute of Computer Applications CHARUSAT India

ISBN: (纸本)9798350372816

The escalating environmental impact of Information and Communication Technology (ICT) has raised concerns about its substantial carbon footprint. Green computing, a concept that focuses on minimizing the environmental impact of computing and IT infrastructure, has emerged as a promising approach to address this issue. This research paper investigates the potential of integrating green computing practices to curtail the environmental ramifications of ICT operations. Understanding the fundamental forces behind the adoption of green computing in business, industry, and government is crucial since ICTs are so critical to technical innovation, the economy, and environmental protection. By examining the principles and methodologies of green computing, this study aims to delineate its role in reducing energy consumption, minimizing e-waste, and fostering eco-friendly practices within the realm of information technology. Examined are the major implementation challenges and the global adoption of green computing. Through a comprehensive review of existing literature and case studies, this research assesses the efficacy of green computing strategies in mitigating the carbon footprint of ICT infrastructures. Furthermore, it evaluates the economic viability and long-term sustainability implications for businesses and society at large. ICT would be a bigger enhancer of productivity and utility with a global carbon limitation, offering opportunity for the industry to be financially successful as a crucial component of a global net zero society. The paper concludes by proposing recommendations for the seamless integration of green computing into ICT ecosystems, emphasizing the pivotal role of stakeholders in fostering a more sustainable digital landscape while simultaneously reducing carbon emissions. © 2024 IEEE.

关键词： Zero-carbon

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A Novel Approach for the safety of Indian Army against Ammunition Inventory 3

A Novel Approach for the safety of Indian Army against Ammun...

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3rd International Conference on Innovative Practices in Technology and Management, ICIPTM 2023

作者： Khant, Shailesh Solanki, Kamini Patel, Atul Faculty of Computer Science and Applications India

ISBN: (纸本)9798350336238

Indian Army is the most important part of our country. Since 75 years of independence the Indian army becomes the most important army for securing nation along with the other security force like Indian Air Force and Indian Navy. During secret missions or any such risk involving activity some care must be taken for their safety. In this paper we are introducing an electronics device which can be weared by the soldier during war situation or secret mission. This device could be used by soldier to send the message to authority or team leader to inform that he is in risky zone. GPS and GSM modules are used to find the latitude and longitude of the soldier's location. Even a message can be sent to higher authority regarding the current situation. ESP32 microcontroller with wireless connectivity is used implement the device. © 2023 IEEE.

关键词： Safety engineering

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Optimizing Climate Data Classification with a Novel Ensemble-based Stacking Classifier 2

Optimizing Climate Data Classification with a Novel Ensemble...

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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Patel, Sanskruti Patel, Unnati Nanavati, Jay Smt. Chandaben Mohanbhai Patel Institute of Computer Applications Faculty of Computer Science and Applications Charusat India

ISBN: (纸本)9798350368413

This research explores the novel machine learning model using stacking classifier with ensemble learning for climate data classification. The proposed model systematically integrates various machine learning techniques, encompassing crucial phases including data preprocessing, feature engineering, and model stacking. By leveraging a combination of logistic regression, Support Vector Machine (SVM), XGBoost and Random Forest models, this research paper demonstrates how ensemble approaches can effectively capture complex patterns within climate data. The dataset, sourced from the National Oceanic and Atmospheric Administration's (NOAA) data repository of hurricane data, includes a wide array of features related to hurricanes and tropical storms, making it ideal for analyzing and predicting storm status. The use of a Stacking Classifier combining multiple models (logistic regression, Random Forest, SVM, XGBoost) appears to be quite effective, achieving 95.74% accuracy, outperforming individual models and simpler ensemble methods. The research establishes the prospective of machine learning techniques to improve climate data analysis and prediction accuracy. © 2024 IEEE.

关键词： Adversarial machine learning

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Designing Embedded System Laboratory using Arduino Controller for Online BCA Education 4

Designing Embedded System Laboratory using Arduino Controlle...

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4th International Conference on Innovative Practices in Technology and Management, ICIPTM 2024

作者： Patel, Atul Khant, Shailesh Charusat University Faculty of Computer Science and Applications Changa India

ISBN: (纸本)9798350307757

Many of the Indian Universities have started Online under-graduation and post-graduation courses after COVID19. Many such universities are Amity University, Charusat University and Parul University. Government (UGC and AICTE) has given permission for online BCA, BBA and Engineering programmes. BCA and Engineering course especially introduce a course where online tool are used. One such subject becomes popular among the placement companies is Embedded System design using different controllers. Here a newer concept of virtual laboratory for Embedded system using Arduino controller is explained. This paper includes various embedded system experiments and applications which is to be implemented using online tool Tinkercad. Various input devices like sensors and output devices like actuators are simulated here using online tool. This paper will help students and researchers to develop virtual embedded system projects without purchasing the actual components. Projects based on Ultrasonic sensor, Gas sensor, PIR sensor, Servo motor and many more are simulated here. © 2024 IEEE.

关键词： Embedded systems

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Exploring Challenges in Modern High Speed Free Space Optical Communication 5

Exploring Challenges in Modern High Speed Free Space Optical...

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5th IEEE Global Conference for Advancement in Technology, GCAT 2024

作者： Patel, Tejas Khant, Shailesh Patel, Atul Charusat Faculty of Computer Science and Applications Changa Anand India

ISBN: (纸本)9798350376685

Free-Space Optical (FSO) systems have gained significant importance in the field of due to their unique attributes. They provide a wide spectrum, increased bandwidth, high data rates, lightweight design, lower power consumption, and rapid deployment capabilities. FSO systems are especially advantageous for disaster recovery, defense purposes, addressing connectivity challenges in remote areas, and remote sensing. However, despite their many benefits, the effectiveness of FSO systems is impacted by challenges like beam deviation, spread, scattering, atmospheric turbulence, and alignment inaccuracies. These hurdles result in notable fluctuations in both Bit Error Rate (BER) and Signal-to-Noise Ratio (SNR), making the communication link impractical. To enhance the performance of FSO communication systems, this study delves into various channel models, modulation methods, and strategies to counter atmospheric disturbances such as turbulence, fog, absorption, and scintillation. The research discusses diverse modulation and diversity techniques in the. Additionally, it presents a comparative examination of SNR and BER influenced by various atmospheric factors affecting FSO systems. The primary goal of this survey is to offer an in-depth guide for developing cost-effective, high-capacity FSO connections. © 2024 IEEE.

关键词： Atmospheric turbulence

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Analyzing Generative Models for Realistic Data Augmentation across Modalities and applications 18

Analyzing Generative Models for Realistic Data Augmentation ...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Patel, Sanskruti Patel, Unnati Nanavati, Jay Patel, Atul Smt. Chandaben Mohanbhai Patel Institute of Computer Applications Faculty of Computer Science and Applications Charusat Gujarat India

ISBN: (纸本)9789380544519

In the emerging era of artificial intelligence, Generative AI models such as Transformer-based models, Generative Adversarial Networks, Diffusion models and Variational Autoencoders offer a powerful paradigm for data augmentation by enabling the generation of artificial data that be like to the original data distribution. This study discusses the use of generative models in place of traditional augmentation techniques and reviewing their history and drawbacks. It explains the workings of GANs and VAEs, and discusses strategies for effectively integrating them into the augmentation pipeline to boost model performance. Both perceptual metrics like Inception Score and Frechet Inception Distance as well as domain-specific metrics like F1-score, IoU and MSE are presented for rigorously evaluating the quality of generated data. Through case studies and experimental results, the paper demonstrates quantitatively how generative augmentation can enhance dataset diversity, prevent overfitting on limited data, and significantly improve metrics across tasks like classification and object detection. It discusses challenges and future scope of generative models for data augmentation. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Data Augmentation GANs Generative Models VAEs

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Combined deep convolutional neural networks for abnormality classification in wireless capsule endoscopy images

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Multimedia Tools and applications 2025年 1-29页

作者： Garbaz, Anass Lafraxo, Samira Charfi, Said El Ansari, Mohamed Koutti, Lahcen Salihoun, Mouna LabSIV Department of Computer Science Faculty of Sciences Ibn Zohr University Agadir Morocco Informatics and Applications Laboratory Department of Computer Science Faculty of Sciences My Ismail University Meknes Morocco Faculty of Medicine and Pharmacy of Rabat Mohammed V University Rabat Morocco

A digestive disease is a condition that affects the gastrointestinal (GI) tract, ranging from mild to severe. Common issues include heartburn, cancer, irritable bowel syndrome, and lactose intolerance. Additionally, other intestinal concerns such as bleeding, polyps, celiac disease, Crohn’s disease, ulcers, diverticulitis, small bowel syndrome, and intestinal ischemia represent further digestive illnesses. Wireless capsule endoscopy (WCE) is a device designed for endoscopic exploration that employs a compact camera to capture images of the GI tract as it passes through. WCE significantly aids in the identification of GI tract disorders. However, due to the capsule’s rapid transit and the potential for blurry, ambiguous images, abnormalities in certain regions of the intestine may be overlooked. Moreover, frames can become hazy if obstructed by food or stool waste. In this research, we propose a method for detecting abnormalities in WCE images to address these limitations. To achieve high classification performance through the extraction of both high-level and low-level features, we employ a deep CNN that integrates the DenseNet121 model with a residual block. Experimental studies demonstrate that our proposed method outperforms state-of-the-art techniques on two publicly available WCE datasets, one focused on bleeding and the other on various abnormalities. The accuracy of the proposed architecture is 96.96% for the Kvasir-Capsule Dataset and 99.50% for the MICCAI 2017 dataset. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Endoscopy

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