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共 40654 条记录，以下是201-210 订阅

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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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Weather Forecasting Web application 4

Weather Forecasting Web application

引用

4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Gowrav, N. Dhanush, V. Chavan, Chandrashekhar Pomu Pes University Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331518981

Weather Refers to the Atmosphere state of certain area and time in terms of temperature, clouds, winds, dry state, sun and rain. The most important geophysical phenomenon that affects us is weather. Weather fluctuates greatly and is impacted by a number of elements including cloudiness, climate, and other variables. The primary goal of the project is to develop an app that can deliver weather forecasts for each city on the planet. We wish to use the ReactJS java script technology to create a web application. © 2024 IEEE.

关键词： Java programming language

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

Performance Comparison of Machine Learning Algorithms for Pa...

引用

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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Hybrid Image Encryption for IoT Applications: Integrating Cellular Automata and Henon Map to Improve Security and Performance 4

Hybrid Image Encryption for IoT Applications: Integrating Ce...

引用

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

作者： Yogi, Biswarup Khan, Ajoy Kumar Roy, Satyabrata Mizoram University Dept. of Computer Engineering Mizoram Aizawl India Manipal University Dept. of Computer Science and Engineering Rajasthan Jaipur India

ISBN: (纸本)9798350387490

Securing the vast volumes of data exchanged over networks is crucial in IoT (Internet of Things) applications. This study introduces an innovative image encryption algorithm that combines the interactive properties of Cellular Automata (CA) and the Henon map to improve both security and performance. CA, renowned for its simplicity and parallelism, are utilized to produce intricate pseudorandom sequences. These sequences are then used as the keystream for encryption purposes. Whereas, the Henon map is employed to incorporate further levels of unpredictability and intricacy into the encryption procedure. The proposed method is thoroughly assessed through considerable experimentation and is compared with existing methodologies. The findings demonstrate substantial enhancements with respect to various standard metrics such as 'Mean Squared Error (MSE)'., 'Peak Signal-to-Noise Ratio (PSNR)'., 'Unified Average Changing Intensity (UACI)'., and 'Number of Pixels Change Rate (NPCR)' measurements., underscoring the resilience and efficacy of the proposed methodology. The results emphasize the capability of this hybrid method to offer a secure and robust performance for image encryption in IoT applications. This method guarantees the integrity and confidentiality of data while outperforming current encryption techniques in performance. © 2024 IEEE.

关键词： Mean square error

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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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Cyberbullying Detection and Severity Classification Using Bi-LSTM 9

Cyberbullying Detection and Severity Classification Using Bi...

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9th International Conference on Communication and Electronics systems, ICCES 2024

作者： Devi, K. Nirmala Rajasekar, Vani Jayanthi, P. Balasubramani, Kavin Kandasamy, Kaviya Gowrisankar, Keerthana Kongu Engineering College Dept. of Computer Science Erode India

ISBN: (纸本)9798350377972

Cyberbullying has grown to be a significant issue in the internet age, affecting individuals of all ages, particularly teens. It involves using internet channels to harass, threaten, or disparage people, often with lingering emotional and psychological repercussions. In social media platforms cyber bullying is becoming more and more common, which has raised the need for efficient tools to identify and remove harmful content. Based on the frequency of objectionable words, a system was created for this research to categorize tweets into four groups: neither, low, medium, or high. A Kaggle dataset was first cleaned and prepared by pre-processing with Keras and Natural Language Toolkit (NLTK). The offending severity level of the tweets was determined using a fuzzy logic with a triangular membership function. Several models were trained using that categorized datasets like Bidirectional Long Short-Term Memory (Bi-LSTM), Bidirectional Encoder Representations from Transformers (BERT-Base) and DistilBERT. Among those models Bi-LSTM showed a high degree of accuracy with a precision score of 95.52%, recall of 95.48%, accuracy score of95.48%, and F1 score of 95.49%. © 2024 IEEE.

关键词： Deep learning

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An Ensemble Machine Learning-Based Method to Determine the Presence of Heart Ailment Using Pooled Dataset

An Ensemble Machine Learning-Based Method to Determine the P...

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2024 IEEE International Conference on Computing, Applications and systems, COMPAS 2024

作者： Saad, Mirza Nadim Jahan, Esrat Ahmed, Shifat Mohi Uddin, Khandaker Mohammad Southeast University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798331529765

Heart disease remains the leading cause of mortality globally, emphasizing the critical need for better diagnostics. This study used a variety of machine learning methods to forecast cardiac disease, using SVM, Random Forest, Decision Tree, KNN, and LGBM. The evaluation measures include accuracy, ROC, precision, recall, and F1 score. Data pretreatment procedures such as cleansing, transformation, integration, and reduction were used to guarantee reliable analysis. The model's performance was further enhanced by hyperparameter tuning. SVM had the lowest performance of the models examined, with 57.23% accuracy, 86% precision, 89% recall, and an F1 score of 87%. The study used three different datasets, combining their strengths to improve model discrimination, decrease overfitting, and increase overall accuracy. We developed multiple models from the combination of RF, DT and LGBM. We found accuracy of up to 99.54% by merging LGBM and Random Forest. These findings illustrate the efficacy of the ensemble approaches and data integration in improving heart disease prediction skills using machine learning. © 2024 IEEE.

关键词： Adversarial machine learning

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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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A Study on Deep Fake Face Detection Techniques 3

A Study on Deep Fake Face Detection Techniques

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

作者： Arya, Mudit Priyanshu Upwan Akash Goyal, Umang Chawla, Simran Chandigarh University Dept. of Computer Science and Engineering Punjab India

ISBN: (纸本)9798350375190

Advanced technology and the widespread use of deep fake technology has rendered the digital landscape vulnerable to deceptive manipulations, particularly through the creation of synthetic face images. This comprehensive review precisely traces the historical trajectory of deep fake faces, encompassing their evolution and its impact on various domains. It also investigates into the growing concerns regarding misinformation and privacy breaches. The literature review analyzes important works, pivotal milestones, and notable case studies, providing a complete understanding of the dynamic landscape of deep fake faces. This study analyzes existing detection techniques, ranging from neural networks to machine learning approaches, offering insights into the complexities of current methodologies. It sheds light on both the strengths and limitations of these techniques, emphasizing the need for robust solutions to counter adversarial attacks and address data scarcity. Building on this analysis, this study explores breakthroughs within the field of deep fake detection, highlighting instances where identification has been successful. It also underscores the ethical considerations that come with the evolution of these technologies. In proposing innovative solutions and discussing potential optimizations for existing techniques, the review explores interdisciplinary approaches and emerging technologies like blockchain and explainable AI. The findings provide a summary of key insights collected from the literature, identify research gaps, and project the future of deep fake face detection. This study concludes by emphasizing the ongoing societal impact of these developments in the digital era. © 2024 IEEE.

关键词： Face recognition

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