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Integrating Artificial Intelligence and Educational Technology for Inclusive and Ethical Education Practices 3

Integrating Artificial Intelligence and Educational Technolo...

引用

3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Ilayaraja, Lathika Yukthika, S.S. Prabakaran, Subramani Pavithra, S. Dept. of Computer Science and Engineering Chennai Institute of Technology Tamil Nadu Chennai600069 India

ISBN: (纸本)9798331527549

AI and EdTech are transforming education by making it more accessible, personalized, and assessable, even in low and middle income countries (LMICs) facing resource constraints. However, challenges come with unequal access, issues in data privacy, and bias in AI model creations. As AI continues to become more popular, the issue of whether the work made by the students will be original. This paper focuses on the impact of artificial intelligence on learning in both its ethical and practical dimensions while highlighting the themes of inclusivity, fairness, and authenticity. Guidelines and policies for responsible AI in integration will be proposed further, alongside teacher training. Results should depict that this moral framework, policies, and teacher education should aid in ensuring that the implications of AI are positive and fruitful for learning. Ideal AI guidelines would foster an educational system that is inclusive, transparent, and considers uniqueness and innovation. © 2025 IEEE.

关键词： Federated learning

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Machine Learning Techniques for Identifying Child Abusive Texts in Online Platforms 15

Machine Learning Techniques for Identifying Child Abusive Te...

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Mahmud, Tanjim Akter, Tahmina Uddin, Mohammad Kamal Aziz, Mohammad Tarek Hossain, Mohammad Shahadat Andersson, Karl Rangamati Science and Technology University Department of Computer Science and Engineering Rangamati4500 Bangladesh Port City International University Dept. of Computer Science and Engineering Chittagong Bangladesh Chittagong University of Engineering & Technology Department of Computer Science and Engineering Chittagong Bangladesh University of Chittagong Dept. of Computer Science and Engineering Chittagong4331 Bangladesh Luleå University of Technology Cybersecurity Laboratory Luleå97187 Sweden

ISBN: (纸本)9798350370249

This study addresses the critical issue of child abuse in digital communications by developing advanced machine learning and deep learning models for detecting child abusive texts in the Bengali language on online platforms. The primary goal of this research is to contribute to child abuse prevention by creating an effective tool for accurately identifying abusive content. Utilizing a combination of natural language processing (NLP) techniques and deep learning algorithms, this model distinguishes between abusive and non-abusive text. The dataset, comprising manually collected and labeled comments and posts from social media sites, is categorized into Positive, Neutral, and Negative classes. For model development, a variety of machine learning algorithms including RF, DT, and SVM, alongside deep learning approaches such as LSTM and CNN, were tested. The performance of the proposed model was assessed using precision, recall, and F1 score metrics. The findings reveal that the model performs with high accuracy in classifying abusive text in Bengali, offering significant potential as a tool for effective child abuse identification and prevention. © 2024 IEEE.

关键词： Deep Learning Machine Learning Natural Language Processing Sentiment Analysis Supervised Model

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Earthquake Prediction Through Machine Learning Approach 4

Earthquake Prediction Through Machine Learning Approach

引用

4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Shrote, Pratik Dasarwar, Priya Dongre, Snehlata Khobragade, Prashant Intern Center of Excellence Data Science Gh Raisoni College of Engineering Nagpur India Nagpur Campus Pune India Gh Raisoni College of Engineering Dept. of Computer Science and Engineering Nagpur India H Raisoni College of Engineering Dept. of Computer Science and Engineering Nagpur India

ISBN: (纸本)9798350387490

The population of earthquake-prone areas is growing, as is the number of people killed and injured by earthquakes. The problem of the increasing number of populated regions that are vulnerable to earthquakes is being addressed by a shortage of experts in earthquake engineering. This implies the requirement for automated intelligence-monitored mitigation and warning systems. Predicting earthquakes is still a major problem for disaster management around the globe. The objective of this work is to build a machine learning (ML) approach that uses geographic coordinates (latitude, longitude) and elevation (height) as inputs to forecast earthquake magnitude. The estimated magnitude is used to classify the seismic risk level for a given site into high, low, or no risk categories using Richter's scale. The study offers a thorough analysis of the methodology, covering feature engineering, data collecting, preprocessing, and model construction. A variety of machine learning methods are investigated, and relevant measures are used to assess their effectiveness. © 2024 IEEE.

关键词： Adversarial machine learning

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Multi Layered Defense Architecture for DDoS Protection in Private Cloud Environments 2

Multi Layered Defense Architecture for DDoS Protection in Pr...

引用

2nd IEEE International Conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Singh, Shashwat Sharma, Anubha Soni, Shani Prakash, Ayushi Ajay Kumar Garg Engineering College Dept. of Computer Science and Engineering Uttar Pradesh Ghaziabad India

ISBN: (纸本)9798350368109

Private cloud solutions have increasingly been adopted because of the demand from organizations for enhanced data security, control over regulatory compliance, and IT infrastructure has been rising. Although public cloud services offer built-in security solutions, private clouds must possess a custom built architectures to address critical challenges, including Distributed Denial of Service (DDoS) attacks, among *** are often deployed behind private cloud environments without the benefits of prebuilt SaaS based DDoS protection. In this exposition, we present an architecture for multilayer protection against DDoS attacks in private cloud environments, which includes the following key components: Global Load Balancer, Traffic Scrubbing Center, Signature based IDPS, and Command and Control Center. Further, we also present a custom Command and Control Center, which utilizes CNNs for runtime handling of network traffic flow, based on real-time monitoring and activity patterns, to mitigate the menace of DDoS. Experimental results show that the architecture is efficient in filtering volumetric and application layer DDoS attacks, ensuring a malicious packet drop rate greater than 95%, while maintaining latency below 150 ms. © 2024 IEEE.

关键词： Cloud security

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Dynamic Landslide Prediction, Monitoring, and Early Warning with Explainable AI: A Comprehensive Approach 3

Dynamic Landslide Prediction, Monitoring, and Early Warning ...

引用

3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Binu, Kristin Elizabeth Anoopkumar, Lakshmi Thampi Sunil, Maria Jose, Merin Preetha, K.G. Rajagiri School of Engineering & Technology Dept. of Computer Science and Engineering Rajagiri Valley PO Kochi India

ISBN: (纸本)9798350375190

Landslides are a major threat to infrastructure, human lives, and the environment. Therefore, early warning systems and precise forecasting are essential for reducing their effects. This study investigates how to enhance landslide prediction models using Explainable Artificial Intelligence (XAI) methodologies. Intending to improve complex machine learning models' interpretability and transparency, XAI seeks to improve comprehension and confidence in the model's predictions. XAI methodologies used are SHapley Additive exPlanations (SHAP), LIME (Local Interpretable Model-agnostic Explanations), and feature importance analysis. These techniques produce comprehensible explanations for each forecast and assist in determining the most significant variables in landslide prediction. The proposed XAI-enhanced landslide prediction model demonstrates promising results in terms of accuracy and interpretability. The research reported here helps to design more potent early warning systems for landslides by advancing explainable AI applications in geoscience and disaster risk reduction. © 2024 IEEE.

关键词： Landslides

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Land Use Land Cover Classification Using Machine Learning and Remote Sensing Data: A Case Study of Karnaprayag, Uttarakhand, India 1

Land Use Land Cover Classification Using Machine Learning an...

引用

1st International Conference on Electronics, Communication and Signal Processing, ICECSP 2024

作者： Saini, Rashmi Singh, Suraj Govind Ballabh Pant Institute of Engineering and Technology Dept. of Computer Science and Engineering Pauri Garhwal India

ISBN: (纸本)9798350364590

Land use and land cover (LULC) classification is crucial for planning and sustainable development, especially in mountainous regions. This study focuses on the Tehsil Karnaprayag, situated in the vicinity of the Greater Himalaya. With a total area of 1858.903 km2 and elevations ranging from 647m to 7641m. Using multispectral Sentinel-2 imagery and Machine Learning techniques i.e. Extreme gradient boosting (XGBoost) and K-nearest neighbor (KNN). This study aims to classify seven distinct land cover classes: snow, forest, water bodies, grassland, agricultural land, fallow land, and built-up areas. The classification results demonstrate the effectiveness of XGBoost classifier achieving an accuracy of 90.22% and a kappa value of 0.882, while KNN achieved an accuracy of 88.59% with a kappa value of 0.862. The analysis reveals that forest and fallow land are the predominant land cover types in the study area, constituting nearly 60% and 15% of the total area respectively. Notably, XGBoost exhibits the highest class-specific accuracy of 97.60% for the snow class, demonstrating its utility in discriminating between challenging land cover types. This research highlights the importance of LULC classification in informing land management strategies and facilitating sustainable development initiatives in mountainous regions. The findings provide valuable insights into the spatial distribution and composition of land cover in Tehsil Karnaprayag, essential for effective planning and conservation efforts in the Himalayan ecosystem. © 2024 IEEE.

关键词： Sustainable development

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Comparative Advances in Financial Sentiment Analysis:A Review of BERT,FinBert, and Large Language Models 3

Comparative Advances in Financial Sentiment Analysis:A Revie...

引用

3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Mahendran, Manish Barath Gokul, Aswin Kumar Lakshmi, Poornima Pavithra, S. Dept. of Computer Science and Engineering Chennai Institute of Technology Tamil Nadu Chennai600069 India

ISBN: (纸本)9798331527549

Capturing market sentiments and supporting well-informed financial decision-making depend on the developing field of Financial Sentiment Analysis (FSA). Natural language processing (NLP) has made significant strides in comprehending and categorizing sentiment in intricate financial texts, especially with the use of Large Language Models (LLMs). The application of various LLMs, such as Bidirectional Encoder Representations form Transformers(BERT) and Financial BERT (FinBERT), as well as distilled models, such as DistilBERT and DistilRoBERTa, on a variety of financial datasets, including Financial Phrase Bank and LexisNexis news articles, was the main focus of this review article. highlighting different approaches such as model fine-tuning, zero-shot and few-shot learning, and prompt engineering. The study focuses on practical model predictions through a case study of sentimental analysis of cryptocurrencies. While FinBERT, a financial variant of BERT, exhibits high accuracy and robustness, other LLMs exhibit varying degrees of success based on the dataset and domain requirements. The analysis concentrates on the difficulties, compromises, and potential paths for improving LLMs for financial sentiment analysis. © 2025 IEEE.

关键词： Zero-shot learning

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Advantages of MIMO Radar for Contactless Pulse Waveform Monitoring using End-to-End Learning

Advantages of MIMO Radar for Contactless Pulse Waveform Moni...

引用

2025 IEEE Radio and Wireless Symposium, RWS 2025

作者： Bauder, Chandler J. Fathy, Aly E. University of Tennessee Knoxville Dept. of Electrical Engineering & Computer Science KnoxvilleTN United States

ISBN: (纸本)9798350377743

Radar-based methods for contactless pulse monitoring typically use heart rate estimation accuracy as the primary performance evaluation metric. Recently, more in-dept. metrics such as heart rate variability (HRV) have been of interest, requiring precise pulse waveform reconstructions for reliable estimations. Traditional phase variation-based vital signs signal processing techniques have proven to be susceptible to sources of noise, impacting the shape of the extracted pulse waveform. In this work, the advantages of using raw multi-channel radar data and deep learning in an end-to-end manner for precise pulse waveform reconstruction are explored. Preliminary results show electrocardiogram (ECG) waveforms can be reconstructed from raw in-phase and quadrature 60 GHz multiple-input-multiple-output (MIMO) frequency modulated continuous wave (FMCW) radar data using an end-to-end neural network. Additionally, an ablation study is performed, revealing that waveform reconstruction similarity and estimation accuracy of several HRV metrics generally improve as the number of utilized MIMO channels increases. © 2025 IEEE.

关键词： Electrocardiography

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Support Vector Regression Based Traffic Prediction Machine Learning Model∗ 8

Support Vector Regression Based Traffic Prediction Machine L...

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

作者： Srivastava, Ansh Singh, Mrigaannkaa Nandi, Somesh Rv College of Engineering Dept. of Computer Science and Engineering Bengaluru India Rv College of Engineering Dept. of Electronics and Communication Bengaluru India Rv College of Engineering Dept. of AI/ML Bengaluru India

ISBN: (纸本)9798331505462

To combat the rising issue of urban traffic congestion, optimization of traffic light timings at intersections is crucial. Traditional methods show stagnancy in managing traffic flow dynamically. This study introduces Support Vector Regression (SVR) as an unconventional solution for traffic signal optimization. The model predicts the traffic conditions and alters the green light to turn on and off accordingly. Utilization of a comprehensive dataset consisting of vehicle distributions and critical flow ratios have been implemented to training a machine learning model. The statistics and performance is checked by usage of Mean Squared Error, R Squared, Mean Absolute Error demonstrating that SVR can effectively enhance traffic signal control. This approach pledges significant improvements in reduction in congestion and streamlined traffic flow. Our findings majorly highlight the capability of the SVR Machine Learning model in improving urban traffic management systems and call for further enhancement and collaboration with experts to address dynamic traffic issues. © 2024 IEEE.

关键词： Mean square error

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Recognition of Handwritten Punjabi Characters Using Machine Learning Techniques 1

Recognition of Handwritten Punjabi Characters Using Machine ...

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Kaushik, P. Kaushik, Manas Manglik Chauhan, Yuvraj Singh Kumar, Narendra Kumar, Ashwani Rathore, Rachna Chandigarh University Dept. of CSE Chandigarh India Manipal University Dept. of Mechanical and Industrial Eng Karnataka India Chandigarh University Dept. of Computer Science Engineering Chandigarh India Usha Martin University Dept. of Computing and IT Jharkhand India Mangalayatan University Dept. of Computer Eng. and Applications Aligarh India Shrimant Madhav Rao University Dept. of Mathematics Gwalior India

ISBN: (纸本)9798350356816

Detecting handwritten Punjabi alphabets (PbAD) presents significant challenges for text detection systems due to the similarity among many characters and their complex curves and edges. Existing automatic detection systems often struggle to deliver high accuracy with real-world data. In this study, we propose an approach for PbAD that synthesizes various techniques reviewed from existing literature to optimize accuracy. Our methodology involves evaluating and combining multiple classification and feature extraction techniques, as identified through an extensive literature review. Unlike many studies that rely on manually created datasets, our approach will leverage both available online datasets and those developed in previous research. Previous research has reported accuracies ranging from 80% to 99%, and our goal is to advance these results by integrating and refining the best practices from the literature. © 2024 IEEE.

关键词： Adversarial machine learning

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