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共 35282 条记录，以下是181-190 订阅

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VisualBlend - A Mixed Approach on Forensic Image Generation and Recognition 6

VisualBlend - A Mixed Approach on Forensic Image Generation ...

引用

6th IEEE International Conference on Recent Advances in Intelligent Computational Systems, RAICS 2024

作者： Unnikrishna, Pillai Midhun Pulkuzhiyil, Vinayak Krishna, Jyothika Wilson, Naveen Kumar, Veena A. Saintgits College Of Engineering Dept. of Computer Science and Engineering Kottayam India

ISBN: (纸本)9798350381689

An extensive use case of Generative Adversarial Networks (GANs) that not only combines artistic creativity with technological innovation but also significantly improves identification precision, filling in critical gaps in the smooth integration of textual descriptions with visual representations. The motivation is rooted on the difficulties that currently exist in accurately identifying human face sketches by directly transforming them into images, an area where prior approaches were inadequate. Our special dual-function technology is designed to get beyond these obstacles and provides a fresh approach for forensic investigators, artists, and sculptors. It has the ability to convert text to image as well as sketch to image. This easy transformation of textual and drawing data represents an integrated combination of creativity and technology that is set to have an impact on a number of disciplines, such as suspect identification and crime scene reconstruction. © 2024 IEEE.

关键词： Generative adversarial networks

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Analyzing and Visualizing Causes of Poverty in India: A data-Driven Approach Using HPCC Systems 8

Analyzing and Visualizing Causes of Poverty in India: A Data...

引用

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

作者： Skanda, P.R. Shobha, G. Shetty, Jyoti Rv College of Engineering Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331505462

Poverty in India is a complex issue with multiple causes. This paper mainly focuses on three of the main causes for poverty: Education, Unemployment and Consumer Price Index. This study makes use of data received from the National Informatics Center (NIC) of India to predict poverty across the various states of India. Through applying preprocessing techniques and employing the HPCC Systems Visualization bundle, the research shows that Bihar is facing a higher level of poverty, whereas Mizoram exhibits converse trend. Subsequently, the HPCC ML-Core bundle is applied to conduct linear regression analysis for predicting poverty scores. This predictive model demonstrated an accuracy of 97%. Lastly, the difference between a single-node and 8-node cluster is presented for runtime performance which clearly shows that the 8-node cluster has an advantage for similar computational tasks and thus makes it suitable. © 2024 IEEE.

关键词： Visualization

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Enhanced Text-Summarization and Language Simplification:End-to-End Approach for Content Comprehension 1

Enhanced Text-Summarization and Language Simplification:End-...

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1st International Conference on Trends in engineering Systems and Technologies, ICTEST 2024

作者： Riza Ayisha, P. Mathew, George Government Engineering College Dept. of Computer Science and Engineering Thrissur India

ISBN: (纸本)9798350307795

Text summarization is the process of shortening a large body of text in a document to form a shorter while preserving its most important information. With the exponential growth of digital data, there is a need for automated text summarization to help users quickly comprehend the content. The use of machine learning techniques, particularly deep learning models, has shown great promise in generating high-quality summaries. Among these models, BERT (Bidirectional Encoder Representations from Transformers) has emerged as a state-of-the-art method for various natural language processing (NLP) tasks, including text summarization. In this paper, we propose to implement a text summarization system using the BERT model. Apart from the normal text summarization, we will be implementing a Language Simplification model that simplifies complex sentence structures or use simpler vocabulary while preserving the original meaning. This model transforms convoluted or verbose sentences into shorter and more straight-forward constructions The paper aims to develop an end-to- end summarization system that takes a long document as input and generates a summary of the most important information contained in the document. For example, like taking the entire news from an online portal and generates it summary. © 2024 IEEE.

关键词： Signal encoding

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Detection of Cardiovascular Disease from ECG Image Using Optimized Weighted Average Ensemble Machine Learning Technique 26

Detection of Cardiovascular Disease from ECG Image Using Opt...

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26th International Conference on computer and Information Technology, ICCIT 2023

作者： Hasan, Md. Nahid Hossain, Md. Ali Akash, Ahsan Sadek Rajshahi University of Engineering & Technology Dept. of Computer Science & Engineering Bangladesh Pabna University of Science & Technology Dept. of Computer Science & Engineering Bangladesh

ISBN: (纸本)9798350359015

cardiovascular diseases (CVD) stand as a prominent global cause of death in worldwide. Timely identification and intervention are paramount in dealing with heart disease, as its failure to detect and treated promptly can lead to life-threatening outcomes. The Electrocardiogram (ECG) Signals are mainly used as diagnostic tools that routinely assess the electrical and muscular function of the heart. Until recently, ECG records were predominantly maintained in paper form;thus, manually examining and re-examining the ECG paper records are often proved time-consuming and laborious tasks. Therefore, this research work utilizes, the power of machine learning and deep learning in predicting four different cardiac abnormalities named Abnormal Heartbeats (AHB), History of Myocardial Infarction (HMI), Myocardial Infarction (MI), and Normal Heartbeats (NHB) from paper-based ECG images. Different image pre-processing methods have been employed to improve the quality of individual ECG images and a novel lightweight deep neural model is proposed for extracting prominent features from each ECG images. Finally, we used an optimized weighted average ensemble technique for the final prediction. The grid search technique is used to find the optimal parameters and weights of each machine-learning model. Our proposed model shows state-of-the-art results in accuracy, precision, recall, and f1-score of 99.29%, outperforming the existing work. The model has attained an Area Under Curve (AUC) score of 99.96%. © 2023 IEEE.

关键词： Cardiovascular disease convolution neural network (CNN) deep learning ECG image feature extraction image classification machine learning weighted average ensemble

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Single Object Tracking in Satellite Videos Through Deep Learning and Motion Detection Techniques 6

Single Object Tracking in Satellite Videos Through Deep Lear...

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6th International Congress on Human-computer Interaction, Optimization and Robotic Applications, HORA 2024

作者： Hayder, Israa Alizadeh, Fattah Dept. of Computer Science and Engineering University of Kurdistan Hawlere Erbil Iraq Dept. of computer science Toronto Metropolitan university Toronto Canada

ISBN: (纸本)9798350394634

Single object tracking in satellite videos has recently gained a lot of attention in the field of computer vision. Although the satellite images are very informative, the small size of the objects and limited spatial and temporal resolution might make object tracking difficult and inaccurate. In this paper, we propose a method for accurately tracking small objects in satellite videos. In the initial phase, we applied the Fast Super-Resolution Convolutional Neural Network (FSRCNN), a deep learning-based super-resolution technique, to enhance the resolution of the frames. This procedure serves to sharpen the details and features of the objects, resulting in a clearer representation. The main contribution, however, lies in our incorporation of a robust motion detection technique into our framework, marking a significant step forward in this study. The motion detection approach combines various methods and operations with the primary goal of making moving objects distinctly visible and easily distinguishable from the complex image background. This enhancement significantly contributes to the precision and accuracy of the tracking system. We combined both approaches with Fully Convolutional Siamese Network (SiamFC) due to its robustness in tracking processes across frames in videos, capacity to handle changes in target appearance, and ability to deal with small datasets. The Satellite Video Single Object Tracking (SatSOT) and SV248A10-SOT (SV248S), two satellite video single object tracking datasets, were used to evaluate the proposed approach. When the techniques were applied, the SatSOT dataset showed an 11% precision score increase and a 10% success rate improvement. The SV248S dataset exhibited even more significant improvements, with precision scores increasing by 18% and success scores rising by 9%. © 2024 IEEE.

关键词： Satellites

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A Multistage Cascaded Neural Network Model to Identify the Severity of Epileptic Attack

A Multistage Cascaded Neural Network Model to Identify the S...

引用

2024 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems, SPICES 2024

作者： Simon, Martha Thampi, Lidiya Lilly Dept. of Computer Science College of Engineering Trivandrum India Indian Institute of Information Technology Kottayam Dept. of Computer Science Kottayam India

ISBN: (纸本)9798350376135

Epilepsy is a neurological disorder characterized by recurrent seizures, impacting the quality of life for millions of individuals worldwide. Timely and accurate detection of epileptic seizures is crucial for proper diagnosis and effective treatment. Electroencephalogram (EEG) signals provide vital information about brain activity and serve as a valuable tool for epilepsy detection. The methodology opted for this study was an enhanced multilayer feed forward neural network for automatic detection of epilepsy from EEG signals and hence predicting the severity. The authors build a multistage cascaded neural network model for achieving the same. Initially a Simple Artificial Neural Network (SimpleANN) is designed to classify EEG segments into seizure, healthy and interictal. Later a Multilayer Feed Forward Neural Network (MFFNN) is employed for detecting the severity of epileptic patients and hence can be classified into primary and secondary stages. The experimental results showed that the proposed SimpleANN classifier had provided noticeable results, and outperforms all tested classifiers. The study involves the collection of EEG data from a diverse group of patients, including those with epilepsy and healthy individuals. This automated epilepsy identifying system passes through different stages including decomposition, feature extraction for achieving a better performance model. The proposed system was tested and compared with SVM, KNN, MLP and Decision Tree under different measurement metrics. ANN has managed to give an accuracy of 80 - 90% which is significantly higher as compared to other machine learning models. The proposed work can automatically learn complex patterns from EEG data provides an advantage in identifying subtle seizure related features that may not be evident through manual analysis. Furthermore, the model offers real-time capabilities, making it suitable for continuous monitoring of patients at risk of seizures. © 2024 IEEE.

关键词： Electroencephalography

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A Study of Various Optimization Algorithms and Entropy Measures for Image Segmentation 18

A Study of Various Optimization Algorithms and Entropy Measu...

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

作者： Gonthina, Nagamani Prasad, L. V. Narasimha Institute of Aeronautical Engineering Dept. of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9789380544519

One of the most challenging jobs in image processing techniques is image segmentation. To identify the objects of interest in an image, we segment the image into different parts and extract the interesting objects. Previous studies have addressed various techniques for image segmentation, like clustering, thresholding, watershed, neural networks, etc. However, a segmentation technique alone cannot achieve better-segmented results. Much research is being carried out by combining the segmentation technique with either an optimization algorithm or entropy measures to enhance the efficiency of segmented results in the segmentation process. So, there is a need to study various segmentation techniques combined with optimization algorithms or entropy measures. Recent studies focused only on segmentation techniques, entropy measures, or optimization algorithms rather than on these combinations. This paper presents various works based on optimization techniques and entropy measures combined with clustering and thresholding techniques of image segmentation. From the study it is observed that most of the work is carried out in combining thresholding-based image segmentation techniques with optimization algorithms and entropy measures. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Entropy measures Image segmentation Optimization algorithms Segmentation techniques

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Flood Prediction System with Voting Classifier 2

Flood Prediction System with Voting Classifier

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2nd IEEE International Conference on Device Intelligence, Computing and Communication Technologies, DICCT 2024

作者： Akshitha, K. Vijay Anand, M. Saveetha Engineering College Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350372847

Flood prediction plays a vital role in reducing the influence of natural disasters among the communities and the infrastructure. This paper presents a machine learning approach for flood prediction using a voting classifier, combining information from turbidity, gauge height, and discharge as key features. The dataset, comprising historical records of these parameters, serves as the foundation for training and evaluating the predictive models. In our work, we employ a voting classifier, integrating Random Forest and Gradient Boosting algorithms to harness the strengths of diverse models. The ensemble approach aims to enhance prediction accuracy and robustness by aggregating the predictions of individual classifiers. The choice of features turbidity, gauge height, and discharge is motivated by their relevance in capturing hydrological dynamics associated with flood events. The dataset is preprocessed to handle missing values, normalize features, and encode the target variable. The voting classifier is being trained upon a subset of data, and its accomplishment is estimated on a discrete test set. Various metrics, including accuracy, are employed to assess the model's predictive capabilities. Results indicate promising performance in flood prediction, showcasing the potential of ensemble methods for such applications. This system assures an accuracy of about approx. 99% and alert notification will be sent to the residents of flood predicted areas. Hence, this contributes to the ongoing efforts in leveraging machine learning techniques and also provides an alert notification for enhancing disaster preparedness and response with proper communication. © 2024 IEEE.

关键词： Turbidity

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A Blockchain-Based Secure and Transparent Global Radioactive Source Lifecycle Management System

A Blockchain-Based Secure and Transparent Global Radioactive...

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2025 IEEE SoutheastCon, SoutheastCon 2025

作者： Poudel, Susan Kausar, Firdous Das, Debashis Chatterjee, Pushpita Hussain, Sajid Ghosh, Uttam Fisk University Dept. of Mathematics and Computer Science NashvilleTN United States Meharry Medical College Department of Computer Science and Data Science NashvilleTN United States

ISBN: (纸本)9798331504847

The management of radioactive sources is a critical process that ensures the safe and responsible handling of radioactive materials throughout their lifecycle. These sources require careful management from production to disposal, such as real-time tracking and radiation monitoring to follow everyone's safety rules and protect people and the environment. However, these sources present significant global challenges, especially regarding safety, security, and transparency. Current systems face limitations such as fragmented oversight, lack of accountability, and risks of unauthorized access. To address these limitations, this paper proposes a blockchain-based radioactive source lifecycle management system to manage the lifecycle of radioactive sources. Blockchain's decentralized and tamper-proof ledger secures data throughout the entire lifecycle of radioactive materials. By using smart contracts and access controls, the system ensures that only authorized parties can monitor and verify transactions in real-time, which reduces human error and prevents unauthorized changes to the data. Users can perform key operations such as retrieving source details, transferring ownership, updating source locations, and adding observers to our proposed system. Our experiment in designing and testing a blockchain application has proven the potential for a secure and transparent system that enhances global cooperation in managing radioactive sources. Overall, the proposed system not only addresses current challenges in radioactive source management but also enhances the tracking and monitoring of radioactive materials. © 2025 IEEE.

关键词： Radioactive wastes

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Simplifying Digital Streaming: An Innovative Cross-Platform Streaming Solution 15

Simplifying Digital Streaming: An Innovative Cross-Platform ...

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

作者： Muduli, Debendra Sahoo, Saroj Kumar Mishra, Jayanta Kumar Pradhan, Debasish Sharma, Santosh Kumar C.V Raman Global University Dept. of Computer Science Engineering Odisha Bhubaneswar India Centurion University of Technology and Management Dept. of Computer Science Engineering India GITA Autonomous College Dept. of Computer Science Engineering Odisha Bhubaneswar India

ISBN: (纸本)9798350370249

Our research aims to investigate how reinforcement learning methods can be applied to develop a movie recommendation system. We frame the issue of interactive recommendation as a contextual multi-armed bandit, where the system learns user preferences by suggesting new movies and receiving feedback. Through reinforcement learning, we address challenges such as balancing exploitation and exploration and tackling the coldstart problem. Additionally, we incorporate the novelty factor of movies into our model. We evaluate both content-based and collaborative filtering approaches which achieved accuracy 72.33(%), both of which demonstrate effective recommendation outcomes. © 2024 IEEE.

关键词： Contrastive Learning

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