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检索条件"任意字段=International Multi-Conference in Computer Science and Computer Engineering"

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A multi-scale channel interactive attention network for hyperspectral image classification 3

A multi-scale channel interactive attention network for hype...

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3rd international conference on Electronic Information engineering and computer, EIECT 2023

作者： Zhang, Xianhao He, Qinghua Liang, Miaomiao Jiangxi University of Science and Technology School of Information Engineering Ganzhou China China Telecom Stocks Co. Ltd Cloud Network Operation Support Center Ganzhou China

ISBN: (纸本)9798350357707

Hyperspectral data exhibits severely uneven target sizes and subtle spectral differences, which pose deep model challenges in adaptive multiscale contextual semantic perception. In this paper, we propose a channel interaction attention model MCIAN under multi-scale spatial neighborhood perception to improve the recognition performance of multi-scale ground objects. Firstly, multiple filters with different kernel sizes extract the spatial multi-scale information of hyperspectral data in each channel group. Then, the first-order and second-order channel attention mechanisms are introduced on different scales to measure the channel importance and calibrate features from multiple perspectives. Channel grouping leads to limited information interaction between groups. Therefore, partial multiscale feature progressive channel attention recombination and concatenated fusions of other features are further introduced to perform multi-scale information interaction. Finally, the proposed attention module is embedded into a residual backbone network to achieve hyperspectral image classification. The parameter analysis, ablation experiment, and comparative analysis with the state-of-The-Art deep learning methods on three public experimental data sets show that all the modules in our MCIAN have achieved the expected results, and effectively improved the classification accuracy of hyperspectral images. © 2023 IEEE.

关键词： Image classification

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Cardiac MRI Segmentation of Ventricular Structures and Myocardium Using U-Net Variants 5

Cardiac MRI Segmentation of Ventricular Structures and Myoca...

引用

5th international conference on Advanced Research in Computing, ICARC 2025

作者： Wijesinghe, Chinthalanka B. Meedeniya, Dulani Yogarajah, Pratheepan University of Moratuwa Department of Computer Science and Engineering Moratuwa Sri Lanka Ulster University School of Computing Engineering and Intelligent Systems Londonderry United Kingdom

ISBN: (纸本)9798331530983

Accurate segmentation of the ventricular structures and myocardium from Cardiac Magnetic Resonance (CMR) images is essential to diagnose and manage cardiovascular diseases. This study systematically evaluates the performance of five U-Net variants in cardiac MRI segmentation using the Automated Cardiac Diagnosis Challenge (ACDC) dataset and a hybrid loss function combining Cross-Entropy and dice losses. Among the variants, the Feature Pyramid U-Net achieved the best performance, with Dice coefficients of 0.9388 (Left Ventricle), 0.8759 (Right Ventricle), and 0.8426 (Myocardium), showcasing its superior ability to capture multi-scale features and segment complex anatomical structures. The comprehensive and standardized evaluation conducted in this study provides valuable insights into the strengths and limitations of these architectures for cardiac segmentation. © 2025 IEEE.

关键词： Deep learning

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Spine Disease Detection in Spinal X-Ray Images using Machine Learning 13

Spine Disease Detection in Spinal X-Ray Images using Machine...

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13th international conference on System Modeling and Advancement in Research Trends, SMART 2024

作者： Sisodia, Ankur Vishnoi, Swati Khenwar, Medha Chauhan, Dushyant Sharma, Nandini Gupta, Priyanka Gla University Department of Computer Engineering and Applications Mathura India Banasthali Vidyapith Department of Computer Science Rajasthan India G.L. Bajaj Group of Institutions Department of Computer Science and Engineering Mathura India Sanskriti University Department of Computer Science and Engineering Mathura India

ISBN: (纸本)9798350380583

Millions of individuals globally experience spinal ailments. This stresses the importance of swift detection for achieving better treatment results. Traditional diagnosis techniques can be lengthy. They might lack precision. So here the aim is to design a powerful yet budget-friendly system that employs a deep neural network which has been trained on a vast X-ray image database. The system aims at carrying out X-ray scans of spinal vertebrae with improved accuracy. The method proposed utilizes a lite deep neural network to gather information. This is done from numerous levels to aid. In the detection of spinal health conditions, the autonomous system for finding spine illnesses consist crucial elements. These include photo filtering tools attribute excavation methods and efficient classification algorithms. Moreover, Clinical information and patient past records are added to the analyses. This augments the diagnostic precision and dependability of the system. © 2024 IEEE.

关键词： Budget control

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Imbalanced Classes Treatment in Deep Learning multi-label Aspect Classification using Oversampling and Under-sampling

Imbalanced Classes Treatment in Deep Learning Multi-label As...

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2023 international conference on computer science, Information Technology and engineering, ICCoSITE 2023

作者： Af'Idah, Dwi Intan Anggraeni, Puput Dewi Handayani, Sharfina Febbi Dairoh Harapan Bersama Polytechnic Informatics Engineering Tegal Indonesia Harapan Bersama Polytechnic Hospitality Management Tegal Indonesia

ISBN: (纸本)9798350320954

Categorising aspects is one of the research in aspect-based sentiment analysis (ABSA). The aspect classification will categorise the issues addressed by the review content. multi-label categorisation incorporates aspects because each instance will have a unique set of labels or aspects. Aspect classification is one of the ABSA jobs that focuses on classifying the features related to the review text. The most used categorisation strategy relies on balanced data. Unbalanced data can be dealt with in various ways, including algorithm-level and data-balanced methods. This study addresses unbalanced data using three techniques: classification components of SMOTE, ADASYN, and undersampling. This study demonstrates that SMOTE and ADASYN can enhance model performance. However, sampling needs to be more appropriate for LSTM and Bi-LSTM techniques. The results indicate that SMOTE is more suitable than ADASYN for multi-label aspect classifications that employ LSTM or BiLSTM. Comparing the under-sampling approach to using SMOTE or ADASYN, all evaluation scores on LSTM and BiLTSM have been reduced. According to this research, the best model performance for multi-label aspect classification is LSTM with SMOTE implementation for data balancing. © 2023 IEEE.

关键词： Classification (of information)

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Point-based Gesture Recognition Techniques 15

Point-based Gesture Recognition Techniques

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15th international conference on Developments in eSystems engineering, DeSE 2023

作者： Sharma, Varun Kolivand, Hoshang Asadianfam, Shiva Al-Jumeily, Dhiya Jayabalan, Manoj Liverpool John Moores University Department Computer Science Liverpool United Kingdom School of Computer Science and Maths Liverpool John Moores University Liverpool United Kingdom Qom University of Technology Faculty of Electrical and Computer Engineering Qom Iran Islamic Azad University Qom Branch Department of Computer Engineering Qom Iran

Gesture recognition is a computing process that attempts to recognize and interpret human gestures through the use of mathematical algorithms. In this paper, we describe Point Based Gesture Recognition and Point Cloud... 详细信息

ISBN: (纸本)9798350335149

关键词： Gesture recognition

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Improved Denoising for multi-frame Image Restoration with Convolutional Neural Networks 15

Improved Denoising for Multi-frame Image Restoration with Co...

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15th international conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Kalra, Hitesh Maney, Kiran Lokesh Kulkarni, Omkaresh S. Singh, K. Ranjith Ahluwalia, Gunveen Surulivel Rajan, T. Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India School of Mangement - Ug Mangement Karnataka Bangalore India Pune India Karpagam Academy of Higher Education Department of Computer Science Coimbatore641021 India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Mechanical Engineering Chennai127 India

ISBN: (纸本)9798350370249

multi-body photograph restoration is a key application of state-of-the-art image processing. With the improvement of cutting-edge convolutional neural networks (CNNs), it has come to be modern-day viable to repair photos with improved denoising performance. CNNs allow the present-day modern-day complicated non-linear features with high accuracy and fast inference. A CNN-based totally multi-body image healing (MFIR) technique is proposed that is able to seize the spatial correlations between noise and texture in multi-body snapshots. Experiments on benchmark datasets show that our technique achieves overall performance, demonstrating the effectiveness of modern-day CNNs in this challenge. Our approach is powerful to extraordinary multi-frame noise levels and works higher than CNNs with the best unmarried-body entry. Additionally, our proposed technique will be extended to different picture healing responsibilities with diverse today's corpora. © 2024 IEEE.

关键词： Convolutional neural networks

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A Comprehensive Analysis of Hate Speech Detection and Recognition Utilising Advanced Machine Learning Algorithms and Techniques 3

A Comprehensive Analysis of Hate Speech Detection and Recogn...

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3rd IEEE international conference on Automation and Computation, AUTOCOM 2025

作者： Mannepalli, Praveen Kumar Sharma, Saurabh Rajput, Neha Kalamdhad, Sanjay Chandigarh University Department of Computer Science & Engineering Mohali India Chandigarh University Department of Computer Science and Engineering Mohali India Chandigarh University Department of Computer Science and Engineering Punjab India St. Vincent Pallotti College of Engineering and Technology Department of Computer Science & Business System Nagpur India

ISBN: (纸本)9798331542375

Social networking sites have come into demand as a way of sharing ideas and opinions with other people. But unfortunately, people tend to misuse social media to write nasty comments. Victims' lives might be negatively impacted by behaviours that are abusive and humiliating. Social networks give people a great tool to share information on ideas on multiple topics and opinions. However, these platforms are often abused to announce toxic and offensive comments, which eventually negatively affect victims and demographics. This study offers a thorough method for identifying hate speech (HS) on social media by utilising cutting-edge ML algorithms. A publicly available Twitter dataset containing 159,571 tweets classified into six categories, including toxic and identity hatred, was utilised. The methodology comprises data collection, conditioning, and categorisation that employs BiLSTM, which is particularly appropriate for the tasks demanding contextual sensitivity. These operations include symbol removing, stemming and stop-word eliminating, feature extraction using TF-IDF, and using the Bag-of-Words model. For classification of the data between hate and non-hate, the dataset is then split into training and testing datasets and the BiLSTM model used to provide classifications for the text. The evaluation of the BiLSTM model demonstrates superior performance, achieving an accuracy86%, a precision90%, a recall86%, and an F1-score88% • A comparison between the model and other models such as SVM and CNN reveals the strength of the BiLSTM in contextual classification tasks. The results demonstrate that BiLSTM can tackle problems with hate speech detection, providing a strong answer to the problem of harmful content on social media and helping to make the internet a better place for everyone. © 2025 IEEE.

关键词： Tweets

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Cybersecurity Challenges In Healthcare Systems

Cybersecurity Challenges In Healthcare Systems

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2024 international conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Singh, Gurwinder Tiwari, Devesh Goel, Pawan Vishwakarma, Pramod Gupta, Keshav Verma, Aditya Chandigarh University Department Of Aitcse Punjab Mohali India Graphic Era Hill University Department Of Computer Science And Engineering Uttarakhand Dehradun248002 India Raj Kumar Goel Institute Of Technology Department Of Cse Uttar Pradesh Ghaziabad India Chandigarh University Computer Science & Engineering - Apex Punjab Mohali India Grd Institute Of Management And Technology Uttarakhand Dehradun India Graphic Era Deemed To Be University Computer Science And Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350366846

Digital technology are aiding the healthcare business in increasing its efficiency, streamlining its processes, and providing superior treatment to patients in an increasing number of instances. However, as a result of this digital revolution, healthcare systems are now more vulnerable to cyber assaults than they were previously. Some of the cybersecurity risks that are examined in this study in regard to healthcare systems include problems with data breaches, ransom ware, threats from insiders, and compliance with regulatory requirements.. Insider threats, whether intentional or inadvertent, present a formidable challenge to healthcare cybersecurity. Employees, contractors, and third-party vendors with access to sensitive systems and information can inadvertently compromise security through negligent actions or intentionally perpetrate malicious activities. The demand that healthcare organizations conform to stringent data protection standards imposed by regulatory compliance requirements such as HIPAA is another issue that makes cyber security management more difficult to handle. The impact of cybersecurity incidents extends beyond financial costs to encompass reputational damage and patient safety concerns. Healthcare organizations must adopt proactive measures to assess and mitigate cyber risks, including robust risk management frameworks, secure system design principles, encryption protocols, and continuous monitoring mechanisms. © 2024 IEEE.

关键词： Cybersecurity

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Deep Learning-Driven Sentiment Analysis in Textual Data 5

Deep Learning-Driven Sentiment Analysis in Textual Data

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5th IEEE international conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Kathiravan, M. Saravanan, S. Jagadeesh, M. Lakshmi, I. Durga, V. Sathya Raja, N. Bharathi Computer Science and Engineering Chennai India Computer Science and Engineering Saveetha School of Engineering Chennai Thandalam India Computer Science and Engineering Rajalakshmi Engineering College Chennai India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350383522

Emotional recognition from text is essential in natural language processing with far-reaching consequences in areas such as Artificial Intelligence, Human-computer Interaction, and others. Emotions are felt, thought-out physical responses to events. Analyzing these feelings independently of vocal and facial cues is critical to interpreting emotions. Despite these challenges, it is crucial to understand human emotions, especially as people become more comfortable expressing themselves through hate speech on sites like Facebook, Twitter, etc. This paper discusses how to classify many tweets based on their tone. Here, we use deep learning algorithms to determine whether an expression means a happy or sad emotion. There are four distinct negative states of mind: rage, indifference, loneliness, disdain, sadness, and despair. The subset of positive emotions includes zeal, joy, happiness, love, calm, pleasure, and wonder. Using three datasets, we validated and assessed the method's use of long short-term memory and recurrent neural networks to obtain high accuracy in emotion categorization. With an 85.07% predictability for positive and negative classification and an 87.23% and 86.3% accuracy for positive and negative subclasses, respectively, a detailed examination reveals that the system improves emotion prediction using the LSTM model. © 2024 IEEE.

关键词： Deep Learning techniques Emotion Classification Long Short-term memory Recurrent Neural Networks

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Enhanced Health Monitoring System for Internet of Medical Things using Machine Learning 15

Enhanced Health Monitoring System for Internet of Medical Th...

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15th international conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Swedheetha, C. Manikandan, P. Rajalakshmi, J. Muthuraja, T. Kavin Kumar, K. Chandrasekaran, Gokul Sriram Srivathsan, N. Velammal College of Engineering and Technology Department of Computer Science and Engineering India Kalasalingam Academy of Research and Education Krishnankoil Department of Electronics and Communication Engineering Virudhunagar India Sethu Institute of Technology Department of Electronics and Communication Engineering Virudhnagar India Kongu Engineering College Perundurai Department of Electronics and Communication Engineering Erode638060 India Karpagam Institute of Technology Department of Electronics and Communication Engineering Coimbatore India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India

ISBN: (纸本)9798350370249

This brief looks at mobile operation, speech recognition, and voice robotization. Patients with physical limitations, such as Parkinson's disease and cerebral palsy, as well as those with speech impairments, can express their requirements with this application. The initiative enhances the general well-being and independence of people with impairments. Increasing the automation of current technologies is the main objective of this design. © 2024 IEEE.

关键词： Neurodegenerative diseases

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