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5th IEEE International Conference on Civil Aviation Safety and Information technology, ICCASIT 2023

作者： Yue, Wei Yang, Lei Zhao, Xiaofang Dongguan University of Technology School of Computer Science and Technology Dongguan China Dongguan University of Technology School of Electronic Engineering and Intelligence Dongguan China

ISBN: (纸本)9798350310603

The carbon residue formed from calcination of flame retardant materials is crucial to evaluate their performance and ensure quality control. The complex shape and texture variations of the charcoal dross make traditional segmentation methods difficult to perform accurately. We propose an enhanced SwinUNet method to achieve more precise segmentation of the charcoal residue region in flame retardant materials. Improvements involve adapting the original encoder to utilize a convolutional approach and adding a transformer to the skip connection for processing the feature map. Annotate images are fed into an encoder based on a convolutional neural network to extract features, which are then up-sampled in a Swin Transformer-based decoder and connected with shallow feature maps of different scales produced during the encoding phase. Finally, the improved SwinUNet method is compared to several classical image segmentation methods. The results demonstrate significant outperformance of the remaining methods with about a 2% improvement over SwinUnet on Dice. These findings offer an effective tool and guidance for evaluating the performance of flame retardant materials. © 2023 IEEE.

关键词： Image segmentation

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Machine Learning Using Radial Basis Function with K Means Clustering for Predicting Cardiovascular Diseases

Machine Learning Using Radial Basis Function with K Means Cl...

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International Conference on Advances and Applications of Artificial Intelligence and Machine Learning, ICAAAIML 2022

作者： Akoosh, Lamiaa Mohammed Salem Siddiqui, Farheen Naaz, Sameena Alam, M. Afshar Department of Computer Science and Engineering School of Engineering Sciences and Technology Jamia Hamdard New Delhi India

ISBN: (纸本)9789819959730

Heart disease is a condition that affects the heart and has been responsible for most deaths globally in recent decades. Getting a proper diagnosis is an essential step in treating heart disease. The challenge is timely diagnosis and treatment of heart disease in order to save lives. Machine learning algorithms can help healthcare practitioners to predict heart problems using massive medical data. The purpose of this work is to critically discuss and evaluate machine learning algorithms for the diagnosis of several heart disease characteristics using supervised, unsupervised, and deep learning techniques. Further, a neural network model using radial basis function with K means clustering has been implemented using TensorFlow and Keras. The model was run for 10 and 100 epochs for batch sizes 8, 16, and 32 and the results have been derived in terms of loss function and accuracy. Although the model performs very well in terms of loss which is around 0.32, it gives an accuracy of close to 72% only which needs to improve. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： K-means clustering

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Deep Learning for Fraud Detection in Bitcoin Transactions: An AI Approach

Deep Learning for Fraud Detection in Bitcoin Transactions: A...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Shravan, M. Ajitha, E. Arnone, Gioia Renu, Nekkanti Tiwari, Mohit Chouhan, Kuldeep Sr University School of Business Telangana Warangal India St. Joseph's Institute of Technology Computer Science and Engineering India Vrije Universiteit Brussel Department of Private and Economic Law Bruxelles Belgium Department of Computer Science and Engineering Duvvada India Bharati Vidyapeeth's College of Engineering Department of Computer Science and Engineering Delhi Delhi India Sharda University Sharda School of Engineering and Technology Department of Computer Science and Applications Greater Noida201310 India

ISBN: (纸本)9798331508685

This research looks into how deep learning methods can be used to find fake Bitcoin transfers. It's mostly about the unique problems and chances that come with trading in cryptocurrencies and blockchain. Deep learning models are used to look at transaction trends and find oddities that could be signs of fraud. This is because Bitcoin's open structure makes it harder to use traditional methods of fraud detection. We offer a structure that connects CNNs (convolutional neural networks) and RNNs (recurrent neural networks) so that strange patterns can be found in big sets of blockchain transactions. An AI-driven method aims to improve detection accuracy while lowering false positives by taking on scammers' complicated and always-changing strategies. Experienced researchers have shown that the model is good at finding strange things, which means it can be used to safely stop theft in Bitcoin networks in real time. It looks like deep learning could make digital money safer and give us data that can be used in bigger blockchain-based money systems. © 2025 IEEE.

关键词： Adversarial machine learning

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Lane detection networks based on deep neural networks and temporal information

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Alexandria engineering Journal 2024年 98卷 10-18页

作者： Lin, Huei-Yung Chang, Chun-Ke Tran, Van Luan Department of Computer Science and Information Engineering National Taipei University of Technology Taipei106 Taiwan Department of Electrical Engineering National Chung Cheng University Chiayi621 Taiwan School of Engineering Eastern International University Binh Duong Viet Nam

In the past few years, the lane detection technique has become a key factor for autonomous driving systems and self-driving cars on the road. Among the various vehicle subsystems, the lane detection module is one of the essential parts of the Advanced Driver Assistance System (ADAS). Conventional lane detection approaches use machine vision algorithms to find straight lines in road scene images. However, it is challenging to identify straight or curved lane markings in complex environments. To deal with this problem, this paper presents a lane detection technique based on deep learning. It is combined with a 3D convolutional network, so the temporal information is added to the network architecture. Using the front camera images, the system can immediately detect the lane marking information ahead. Moreover, we propose an approach for improvement by adding the time axis to the network architecture. In addition to using 3D-ResNet50, the temporal convolution and spatial convolution are separated for processing. The accuracy is 91.34% improved after adding time and space split convolution with LeakyReLU. The experiments carried out using real scene images have demonstrated the feasibility of the proposed technique for applications to various complex scenes. © 2024 The Author(s)

关键词： Network architecture

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Clustering Model-based Analysis of Sales in Vegetable Categories for Supermarkets 2

Clustering Model-based Analysis of Sales in Vegetable Catego...

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2nd IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024

作者： Liao, Jiana Chen, Jiajun Zheng, Wenxu Ren, Yazhou Guangdong University of Technology School of Automation Guangzhou China University of Electronic Science and Technology of China School of Computer Science and Engineering Shenzhen Institute for Advanced Study China

ISBN: (纸本)9798331515669

The development of effective vegetable sales strategies is crucial for enhancing supermarket profitability, and these strategies are heavily reliant on a nuanced understanding of the sales volume distribution patterns across different vegetable categories. However, most existing studies fail to thoroughly analyze these distribution patterns, often neglecting factors such as sales volume magnitude and fluctuations. To address this gap, we first propose a comprehensive framework based on a K-means++ clustering model and Spearman correlation coefficient to analyze the sales data. Firstly, we normalize the data to eliminate the impact of differing dimensions across vegetable categories. Next, we analyze the sales data from both quarterly and daily perspectives. Data visualization is performed utilizing line charts, followed by a descriptive analysis of the sales volume distribution considering both magnitude and fluctuation. Furthermore, we utilize a K-means++ clustering model and the Spearman correlation coefficient to further explore the distribution patterns and interrelationships among different vegetable categories. The results of the experiments reveal the daily and quarterly sales distribution patterns of various vegetable categories, providing valuable insights to support the development of effective vegetable sales strategies for supermarkets. © 2024 IEEE.

关键词： Sales

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GPS-based On-Demand Ambulance Booking System Using Google Cloud

GPS-based On-Demand Ambulance Booking System Using Google Cl...

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2022 OPJU International technology Conference on Emerging Technologies for Sustainable Development, OTCON 2022

作者： Nauni, Daksh Verma, Shubham Singh, Preetam Gupta, Keshav Sharda School of Engineering & Technology Sharda University Department of Computer Science & Technology Uttar Pradesh India

ISBN: (纸本)9781665492942

Booking an Ambulance is an unreliably long and inefficient process through conventional means. This paper proposes an on-demand ambulance booking system that provides an easy-to-use interface to locate and book the nearestavailable ambulances of varied types on a single tap. The proposed System works on the architecture made of Flutter, Firebase, Postman Testing, and Google Cloud API services. The service allows saving time of around 15-30 minutes. The system has been implemented and the analytics of the implementation has been shared in the paper. There is further scope for increasing the reliability of the system using other devices such as smart wearables to provide a one-tap ambulance booking feature for the user. © 2023 IEEE.

关键词： Flutter (aerodynamics)

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A Novel GPS based Blood Bank Management System 7

A Novel GPS based Blood Bank Management System

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7th International Conference on Trends in Electronics and Informatics, ICOEI 2023

作者： Jha, Sonal Raghav, Siddharth Chauhan, Kartik Kumar Agarwal, Ambuj Kumar School of Engineering and Technology Sharda Univesity Department of Computer Science and Engineering UP Greater Noida India

ISBN: (纸本)9798350397284

The Blood Bank website is an honest attempt to simplify all aspects of receiving and donating blood. Users can readily access information about the availability of blood types in various blood banks throughout the state, as well as the day of donating blood and the option to plan a voluntary blood donation through the website. Almost every medical procedure and emergency circumstance necessitate the use of blood. Because of medical advancements and technology, the number of patients who require blood is increasing so, this is an all-screen-size-supportive internet system that will be utilized to save people's lives by providing information about blood availability, blood seekers, donors, and blood banks. The major goal of the website is to facilitate and speed up communication among users because the blood supply is limited. The GPS (Global positioning system) location of each user in the surrounding region will be displayed to another user. This can assist users in a variety of ways, the most important of which is that blood seekers can simply contact donors and banks or check the nearby availability of blood Blood donors can also contribute easily and communicate via the website's information and discussion sections. Blood donors who intend to donate can check to see if there is an emergency registered and provide assistance in addition to depending on the blood bank. Every registered account's data will be stored in a backend database. The proposed system is designed and further it will be developed with easy-to-use user interface and be helpful to users. It should also make searching process simple and interesting. © 2023 IEEE.

关键词： Websites

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Spatiotemporal Attention Network for Action Recognition Based on Motion Analysis 2

Spatiotemporal Attention Network for Action Recognition Base...

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2nd International Conference on Algorithm, Image Processing and Machine Vision, AIPMV 2024

作者： Yu, Mingxiao Quan, Siyu Wang, Jiajia Li, Yunlong School of Computer Science and Technology Xinjiang University Xju Urumqi China School of Physics and Electronic Engineering Linyi University Linyi China

ISBN: (纸本)9798350390254

Action recognition in video content understanding primarily relies on the information extracted by the model, which needs to encompass both spatial and temporal features. Performance enhancement in deep learning models hinges on effectively learning these crucial spatial and temporal features from videos. Building on this analysis, we propose a spatiotemporal attention network for action recognition based on motion analysis(STAN-ARMA), comprising an optical flow integration module, an optical flow keyframe extraction module, and a spatial attention module. Experimental validation on the UCF-101, HMDB51, UTD-MHAD and HAA500 benchmark datasets demonstrates that the proposed network effectively learns spatial motion characteristics at critical moments in videos, thereby improving the model's recognition performance. © 2024 IEEE.

关键词： Motion analysis

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Liquidt: stock market analysis using liquid time-constant neural networks

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International Journal of Information technology (Singapore) 2024年第2期16卷 909-920页

作者： Gajjar, Pranshav Saxena, Aayush Acharya, Kairav Shah, Pooja Bhatt, Chintan Nguyen, Thanh Thi College of Engineering and Computing George Mason University Fairfax VA United States Electronics and Computer Science University of Southampton Southampton United Kingdom Department of Computer Science & Engineering School of Technology Pandit Deendayal Energy University Gandhinagar India School of Information Technology Deakin University Geelong Australia Institute of Technology Nirma University Ahmedabad India

Accurate and efficient predictions concerning stock prices are an intriguing and sought-after task in the field of computational financial analysis. This paper aims to leverage and validate novel deep learning pipelines for predicting NSE stock prices of Adani Ports, Reliance, and Tata Steel using recurrent neural architectures. The scope of this paper pertains to the utility of Liquid Time-Constant Networks (LTCs) and the development of a novel enhanced computing paradigm centric on LTCs. By assessing the use of bi-directionality for time-series prediction and the existing modality of architectures with LSTM and BiLSTM implications, the authors functioned on developing an LTC-based architecture that is capable of predicting stock market trends in a superlative fashion. The paper thoroughly explains the temporal characteristics and predictive trends and completes a comparative analysis with simple RNNs, GRUs, LSTMs, and their bidirectional counterparts, emphasizing the potential substitutes of the frequently used LSTMs. After sufficient experiments, the paper presents a comparative study for gauging a computational efficiency and accuracy trade-off for the aforementioned task. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Deep learning Liquid neural networks Machine learning Stock price prediction Time series data

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Image segmentation algorithm based on multi-granularity clustering 7

Image segmentation algorithm based on multi-granularity clus...

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7th International Conference on Mechatronics and Intelligent Robotics, ICMIR 2023

作者： Xia, Shuyin Xia, Cenjun Dai, Dawei Ni, Jinyuan School of Computer Science and Technology Chongqing University of Posts and Telecommunication ChongQing400065 China School of Computer Science and Engineering Chongqing University of Technology Chongqing400054 China

ISBN: (数字)9781510668089

ISBN: (纸本)9781510668072

This article proposes an image processing algorithm based on multi-scale clustering, which can improve the probability of a classifier correctly classifying an image even after it has been subjected to noise attacks. The algorithm mainly involves performing multi-scale clustering on the pixels of the image based on features such as color space, distance, texture, etc., and then incorporating the clustering results into the training of the classifier. By directly extracting the main feature information from the image for training, the algorithm prevents large classification errors due to changes in certain pixel values, thus reducing the impact of attacks on other pixels in the image and greatly improving the robustness of the image. Therefore, this algorithm is a robust and efficient denoising method. © 2023 SPIE. All rights reserved.

关键词： Pixels

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