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Integrating Deep Learning Techniques for Enhanced Cyclone Prediction and its Impact on Air Quality Index Utilizing Satellite Imagery

Integrating Deep Learning Techniques for Enhanced Cyclone Pr...

引用

2024 IEEE Students Conference on engineering and Systems, SCES 2024

作者： Vijayakumar, Sushruth Jamadhiar, Sonakshi Raj, Srujan M Mr, Vishnudeep Saritha, K. PES University Dept. of Computer Science Engineering Bengaluru India

ISBN: (纸本)9798350374711

Tropical cyclones (TCs greatly impact ecosystems and weather patterns, often leading to catastrophic outcomes this research explores dynamic relationship between the Air Quality Index (AQI) and Tropical Cyclones (TCs), examining changes in air quality during different stages of TCs. Focusing on Cyclone Biparjoy's impact on India's coastline in June 2023, we uncover its influence on air quality. The study shows good predictive performance by utilizing meteorological data, satellite observations, and machine learning algorithms such as CNN and ConvLSTM. Results show promising outcomes, with CNN achieving an RMSE of about 12.03 and an MAE of approximately 8.523 for TC intensity prediction, and ConvLSTM producing an RMSE of approximately 0.22 and a loss of around 0.04 for TC track prediction. Additionally, regression techniques using meteorological data from Tuetempo for AQI prediction were employed. The ExtraTrees Regression model exhibited strong predictive performance with an RMSE of approximately 19.627, while the Xgboost model had a slightly higher RMSE of around 21.72 our dataset included meteorological data from Tuetempo website for AQI forecasts and datasets like Hursat and INSAT 3D satellite photos for TC-related data, this work offers important insights into the dynamics of air quality during TC events. © 2024 IEEE.

关键词： Tropical cyclone

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Optimizing Kidney Stone Detection with Vision Transformers and CNNs: A Transfer Learning Strategy

Optimizing Kidney Stone Detection with Vision Transformers a...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Kulkarni, Supritha Suhaas Rao, S. Chopra, Tushar Kumar Yeshwanth, S.P. Bhanushree, K.J. Suneetha, K.R. Bangalore Institute of Technology Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331508685

Kidney stones are a common and impactful urological condition, with timely and accurate diagnosis being critical to prevent complications such as renal damage and avoid invasive treatments. This study explores the use of advanced deep learning models, including Vision Transformers (ViT), Swin Transformer and Convolutional Neural Networks (CNNs) - specifically XResNet-50, VGG-16, MobileNetV2 and AlexNet - to enhance kidney stone detection from CT scan images. Leveraging a robust dataset of annotated CT scans in JPG format, the approach employs transfer learning which leverages features learnt from the ImageNet dataset to optimize model performance, focusing on classifying cases as either "Kidney Stone Present"or "Kidney Stone Absent". To further improve diagnostic reliability, the Grad-CAM (Gradient-weighted Class Activation Mapping) identifies and localizes kidney stones within CT images, enhancing understanding of the model's decision-making and supporting interpretability. Addressing challenges related to dataset imbalance, model interpretability, and generalizability across healthcare settings. By refining these models for kidney stone detection, the main objective of this work is to build a reliable diagnostic tool that aids in clinical judgement and treatment planning, ultimately enhancing patient outcomes and encouraging broader clinical adoption. © 2025 IEEE.

关键词： Convolutional neural networks

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A Novel Approach for Driver Drowsiness Detection using Deep Neural Networks 2

A Novel Approach for Driver Drowsiness Detection using Deep ...

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2nd IEEE International Conference for Women in Innovation, Technology and Entrepreneurship, ICWITE 2024

作者： Sharon, K.V. Natarajan, B. Madhumitha, P. Dhanamathi, A. Prabu, S. Saranya, S. Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science and Engineering Chennai India VIT University School of Computer Science and Engineering Tamilnadu Chennai India Panimalar Engineering College Dept. of Artificial Intell. and Data Science Chennai India Roever Engineering College Department of Computer Science and Engineering Tamilnadu Perambalur India K. Ramakrishnan College of Engineering Dept. of Cse Tamil Nadu Tiruchirappalli621112 India

ISBN: (纸本)9798350383287

One major problem that contributes to many traffic accidents that cause injuries and fatalities all over the world is drowsy driving. This research introduces the novel approach for detecting driver drowsiness using deep networks and computer vision techniques aims to solve this issue and improve road safety. The Proposed system employs an InceptionV3-based Convolutional Neural Network (CNN) model to precisely determine whether the driver's eyes are open or closed. A sizable dataset of eye images taken in actual driving situations is used to train the CNN model. Whenever the indicators of drowsiness are detected, the proposed system will identify them in real time and promptly notify the driver. The proposed model not only has strong real-time capabilities but also exhibits superior accuracy based on stringent evaluation metrics, which makes a significant contribution to efforts aimed at promoting road safety. The performance and adaptability of the system are improved and further integrates alarm activation for drowsiness detection in a variety of driving conditions. © 2024 IEEE.

关键词： computer vision

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Leveraging Name Entity Recognition and Text Classification for Talent Mapping 2

Leveraging Name Entity Recognition and Text Classification f...

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2nd IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

作者： Charan, Annepu Sai Moirangthem, Dennis Singh Iiit Manipur Dept. of Computer Science and Engineering Imphal India

ISBN: (纸本)9798350360523

The paper presents a dual-model approach that can be used for categorizing the work experience data from resumes or other textual copra. The application tracking system (ATS), which is the main tool used to screen resumes, presents operational issues in the quickly changing software sector. Although this technique matches resumes with job descriptions effectively, it requires separate scans for every job posting, which could result in errors and inefficiencies. This initiative is motivated by the need to expedite the hiring process for tech companies and potential employees. The suggested approach presents a cutting-edge tool to improve and expedite job matching and resume analysis. The work attempts to improve the software industry's hiring process efficiency by overcoming present system restrictions. This study presents a novel solution - a text classification model intended for resume analysis and shortlisting to address the operational difficulties with the modern resume screening process. Even though the Application Tracking System (ATS) is currently used, it requires separate scans for every job posting, which could lead to inefficiencies. We propose dual-model architecture which includes our custom Name Entity Recognition (NER) Model which is trained on annotated work experience data and a Multi-Layer Perceptron (MLP) model for multi label classification, aiming to streamline the resume screening process. This dual-model approach enhances accuracy and efficiency in resume classification, benefiting both aspirants and tech companies. This research is a pivotal step in optimizing recruitment in the dynamic software industry. The complete work can be found on GitHub1 © 2024 IEEE.

关键词： Mapping

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Automail AI: Intelligent Voice-Controlled Email Generator 3

Automail AI: Intelligent Voice-Controlled Email Generator

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3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Jayaram, Kavitha Mandagere, Samarth Gopisetti, Sampriti Sahit, S. Bnm Institute of Technology Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331515911

The complexity of contemporary communication further emphasizes the need to automate monotonous work to increase efficiency and effectiveness. This paper introduces a new advance, voice-controlled Automail AI, in the form of an email generation tool that automatically sends an email according to end-user-specified keywords. Automail AI utilizes NLP, ML, and Speech Recognition to differentiate formal emails from informal ones and draft appropriate responses as the situation demands. It has been tuned with Naive Bayes Classifier that yields a better level of accuracy as compared to models such as Decision Tree, SVM, and Logistic Regression. Setting the benchmark for Automated Communication tools, Automail AI combines diverse technologies like SMTP to guarantee secure Email delivery, Google Text-to-Speech or GTTS for auditory feedback, and a module providing an interface for speech recognition that offers the possibility to use the tool with no hands. © 2025 IEEE.

关键词： Automail AI email generation GTTS machine learning Naive Bayes natural language processing SMTP speech recognition

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IIoT-based Realtime Environmental Monitoring System for an Underground Coal Mines 15

IIoT-based Realtime Environmental Monitoring System for an U...

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

作者： Thirumal, G. Gautam, Vivek Kumar Kumar, Chiranjeev Seth, Himanshu Ranjan Bodra, Shaily Priyanka Dept. Computer Science and Engg. Jharkhand Dhanbad India Data Science Oyo Vacation Homes Lucknow India

ISBN: (纸本)9798350370249

Underground coal mines are harsh in the working environment, which leads to more causality. Therefore, modern mines are using sensor networks to monitor the mines regularly to minimize mine accidents. Hence, very limited real-time monitoring systems are developed using a Real-time operating system (RTOS). Contiki-NG is a popular embedded application development framework for Wireless sensor networks and the internet of things (IoT). This paper provides a detailed roadmap to design a real-time monitoring system for underground coal using Contiki-NG. The System design comprises designing sensor node with modified RPL protocol and border router using TI CC2650 launchpad and raspberry pi 3. The Node-RED middleware collects data from the application environment using a sensor node. Store the data into the MongoDB database and visualizes sensed data in a web browser in the monitoring center. © 2024 IEEE.

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Performance Comparison of Machine Learning Algorithms for Parkinson's Disease Detection 14

Performance Comparison of Machine Learning Algorithms for Pa...

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14th International Conference on Cloud Computing, data science and engineering, Confluence 2024

作者： Gupta, Ipsha Vashisth, Dhwani Garg, Rakesh Amity University Dept. of Computer Science and Engineering UP India

ISBN: (纸本)9798350344837

Parkinson's Disease affects the neural system and can be detected using biomarkers, physiological and motor disruptions exhibited by patients. Several advancements and research on Machine Learning approaches have been carried out and tested to diagnose PD in the early stages of its inception by continuous monitoring of physical actions of patients. In this study we: (a) conduct a comprehensive analysis of existing machine learning algorithms to detect PD patients, (b) summarize and categorize the findings of efficiency of these algorithms, (c) test these algorithms on speech recordings of patients, (d) provide the model with best accuracy with an overview of all the models previously researched or currently in use. The benefits and shortcomings of the referenced models are thoroughly contrasted and explored for better accessibility for future research. © 2024 IEEE.

关键词： Support vector machines

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BeTS: Abstractive Text Summarization with Transfer Learning 2

BeTS: Abstractive Text Summarization with Transfer Learning

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2nd IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

作者： Bhardwaj, Meher Ethari, Hrishikesh Charan, Annepu Sai Singh Moirangthem, Dennis Iiit Manipur Dept. of Computer Science and Engineering Imphal India

ISBN: (纸本)9798350360523

We introduce BeTS, which stands for Bert Transformer Summarizer, an abstractive summarization model that is built using transfer learning. Our model consists of a pre-trained BERT encoder and a generative transformer decoder to produce abstract summaries. The model is trained by a simple two step process, where the decoder is first trained and then the entire model is fine-tuned end-to-end. We train the model with CNN/Daily Mail summarization dataset and use the trained weights for transfer learning on the AMI dialog summarization dataset. We successfully demonstrate that our model outperforms the baseline models by at least 7.65 ROUGE points on the AMI data. We also demonstrate that our model shows good generalization performance by testing on different unseen input data and compare the generated results. We are able to generate significantly better summaries on inputs such as dialog compared to the current state-of-the-art model. © 2024 IEEE.

关键词： Decoding

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Machine Learning - Driven Approaches for Detection and Analysis of Phishing attacks: A Robust Framework for Cybersecurity 7

Machine Learning - Driven Approaches for Detection and Analy...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Sarvan, N.D.S.S. Sai Ram Reddy, Venkata Jain, Darshan Rakesh, Guntupalli Anguraj, Dinesh Kumar Kl University Dept. of Computer Science Engineering Guntur India

ISBN: (纸本)9798350359299

Phishing is the attempt to trick someone into disclosing confidential and private information by using internet communication, such as bank account details, passwords, credit/debit card details, and usernames. The accuracy of foreseeing such phishing assaults can be substantially enhanced through utilizing these algorithms. The primary objective of this research work is to enhance internet security by providing a dependable and productive method for detecting phishing websites. To achieve this, a machine learning-based approach was employed, utilizing various models trained on a dataset of phishing website attributes. The study evaluated a range of machine learning algorithms and classifiers, including Naive Bayes, Support Vector Machine, K-Nearest Neighbors, Gradient Boosting, Decision Tree, Random Forest, Bagging, Voting, and AdaBoost Classifiers. These evaluation parameters are used to determine the efficiency of the machine learning models including F1 Score, Accuracy, Precision, and Recall values. By training the dataset with key variables from credible websites, the Bagging Classifier algorithm demonstrated a substantial improvement, achieving an accuracy rate of 98.4% resulting in a significant enhancement in phishing website detection compared to other methods. © 2024 IEEE.

关键词： Support vector machines

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Benchmarking Network Functionality: Performance Evaluation of SDN Controllers on Different Network Functions 100

Benchmarking Network Functionality: Performance Evaluation o...

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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Monir, Md Fahad Fawad Hasan, Azwad Hoque, Md Mozammal Ahmed, Tarem Granelli, Fabrizio Independent University Dept. of Computer Science and Engineering Bangladesh University of Houston Cullen College of Engineering TX United States Dept. of Information Engineering and Computer Science Trento Italy

ISBN: (纸本)9798331517786

Network Softwarization revolutionizes infrastructure by introducing programmable network functions, leading to scalable, agile, and efficient networks. This innovation facilitates adaptable, secure Next Generation Networks, transforming connectivity. This study conducts a comparative analysis of SDN-based network functions utilizing three different controllers: Open vSwitch (OVS), OpenFlow Reference controller, and RYU, to understand their impact on packet loss and jitter. The OpenFlow Reference controller, primarily used for educational and basic experimental purposes, serves as a baseline. The OVS controller, operating within the Open vSwitch framework in Mininet, offers a practical level of network management capability, more advanced than the OpenFlow Reference controller yet not as comprehensive as the RYU Controller, a production-grade SDN controller. Our research aims to fill the gap in comparative performance analysis of these controllers, particularly in hybrid round-robin-based load balancing and firewall implementations. Recognizing the operational reality of SDN deployments, we explore both reactive and proactive modes, acknowledging that real-world applications like firewalls and load balancers often operate proactively with pre-populated rules, affecting data plane performance. The findings provide insights into each controller's effectiveness, with OVS and RYU showing enhanced capability in reducing packet loss and jitter compared to the OpenFlow Reference controller. This research not only sheds light on the functional behaviors and performance trade-offs of these controllers but also contributes to the broader SDN and NFV discourse, offering a foundation for future academic and practical advancements in the field. © 2024 IEEE.

关键词： Packet loss

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