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检索条件"机构=Department of Software and Computer Technology"

共 12660 条记录，以下是241-250 订阅

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Tag-Aware Weakly-Supervised Online Hashing with Enhanced Joint Representation

Tag-Aware Weakly-Supervised Online Hashing with Enhanced Joi...

引用

2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Wang, Na Zhan, Yu-Wei Chen, Zhen-Duo Wang, Yongxin Luo, Xin Xu, Xin-Shun School of Software Shandong University Jinan China Department of Computer Science and Technology Tsinghua University Beijing China School of Computer Science and Technology Shandong Jianzhu University Jinan China

ISBN: (纸本)9798350368741

Weakly-supervised online hashing has garnered significant attention recently, yet several challenges remain unresolved, such as how to effectively denoise tags, and how to efficiently learn hash functions in dynamic online scenarios. To tackle these challenges, we propose a novel method named Tag-Aware Weakly-supervised Online Hashing with enhanced joint representation (TA-WOH). Our method creates an enhanced joint representation with CLIP-based features in order to reduce the tag noise. Additionally, we introduce a tag association and noise model for improved similarity matrix and hash code learning. A novel mapping mechanism is developed to align joint representations with the optimal tag space, enhancing both the accuracy and robustness of the model. The computational complexity of TA-WOH is dependent solely on the size of the incoming data, ensuring scalability and efficiency for large-scale datasets. Extensive experiments on two datasets demonstrate that our method surpasses several state-of-the-art methods in both accuracy and efficiency. © 2025 IEEE.

关键词： Learning to Hash Online Hashing Social Image Retrieval

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CONTEXT AWARE FACIAL EXPRESSION RECOGNITION WITH DEEP LEARNING ARCHITECTURES

CONTEXT AWARE FACIAL EXPRESSION RECOGNITION WITH DEEP LEARNI...

引用

2025 International Conference on Energy, Power, Environment, Control and Computing, ICEPECC 2025

作者： Bano, Nudrat Asif, Muhammad Zia, Muhammad Azam Bilal, Hafiz Muhammad Department of Computer Science University of Agriculture Faisalabad Faisalabad Pakistan Department of Computer and Software Engineering Information Technology University (ITU) Punjab Pakistan

ISBN: (纸本)9781837243235

Facial expression recognition (FER) plays a pivotal role in applications such as mental health diagnosis, security, marketing, human-robot interaction, healthcare, education, and gaming. However, challenges like varied facial poses, uneven lighting, and the presence of facial accessories often hinder accurate detection. Traditional methods frequently struggle with effective feature extraction and classification. To address these limitations, this study proposes a robust facial expression recognition architecture based on Convolutional Neural Networks (CNNs) coupled with advanced preprocessing techniques. The model effectively mitigates issues such as lighting variations and class imbalances while achieving enhanced recognition accuracy. A comprehensive evaluation using k-fold cross-validation was conducted on the CK+ dataset, renowned for its high-quality labeled images of primary emotions. The proposed model achieved an accuracy of 96%, significantly outperforming established benchmarks, including VGG-19 (90%), ResNet50 (92%), and MobileNet (94%). These results underscore the efficacy of the CNN-based approach in advancing FER accuracy. Future work will focus on extending this research to real-time facial expression detection, leveraging transfer learning to adapt the model to diverse datasets, and integrating emotion recognition with multimodal data such as speech and EEG signals to broaden its applicability across industries. © The Institution of Engineering & technology 2025.

关键词： CONVOLUTION NEURAL NETWORK DEEP LEARNING EMOTION DETECTION FACIAL EXPRESSION RECOGNITION

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Effectiveness of Deep Learning Technologies to Assist Educational Inclusion for Children with Down Syndrome 5

Effectiveness of Deep Learning Technologies to Assist Educat...

引用

5th International Conference on Advancements in Computing, ICAC 2023

作者： Silva, Shehan Jayasinghe, Anjula Dilshan, Rishitha Dissanayake, Chandur Vidhanaarachchi, Samitha De Silva, Dilshan Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

Down Syndrome (DS) is a genetic disorder causing intellectual disability and developmental delays. Despite instances of discrimination, several individuals with DS have achieved success through proper education and community support. This research addresses the challenges faced by children with DS in terms of motor skill development, cognitive impairments, and speech difficulties with the use of Deep Learning Technologies such as LSTM, CNN machine learning models. To bridge the educational gap and improve speech intelligibility, this study focuses on oral motor exercises that enhance articulation skills. Cognitive development in individuals with DS is also a concern, prompting the proposal of a self-learning system to foster cognitive growth. Fine motor skills improvement is explored through yoga exercises, and interventions like interactive technologies are considered to enhance learning experiences. This paper discusses the development of a user-friendly mobile application for early intervention in DS, integrating interactive activities using advanced technologies and a parent therapy dashboard. The system's potential for improving developmental skills and future expansion is emphasized. © 2023 IEEE.

关键词： Speech intelligibility

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Adv-Triplet Loss for Sparse Attack on Facial Expression Recognition 20th

Adv-Triplet Loss for Sparse Attack on Facial Expression Re...

引用

20th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2023

作者： Li, Weitao Li, Shijun Shang, Lin State Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China Department of Computer Science and Technology Nanjing University Nanjing210023 China

ISBN: (纸本)9789819970247

The susceptibility of current Deep Neural Networks (DNNs) to adversarial examples has been a significant concern in deep learning methods. In particular, sparse adversarial examples represent a specific category of adversarial examples that can deceive the target model by perturbing only a few pixels in images. While existing sparse adversarial attack methods have shown achievements, the current results of sparsity and efficiency are inadequate and require significant improvements. This paper introduces an adv-triplet loss and proposes a search attack method to attack the Face Expression Recognition (FER) system with minimal pixel perturbations. Specifically, we propose an adv-triplet loss function and utilize its gradient information to generate pixels for adversarial examples. Extensive experiments conducted on the CK+ and Oulu-CASIA datasets demonstrate the superiority of our proposed method over several state-of-the-art sparse attack methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Pixels

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Analysis of Decision-Making Strategies in Social Networks

Analysis of Decision-Making Strategies in Social Networks

引用

2025 International Conference on Energy, Power, Environment, Control and Computing, ICEPECC 2025

作者： Zafar, Remsha Zia, Muhammad Azam Asif, Muhammad Department of Computer Science University of Agriculture Faisalabad Faisalabad Pakistan Department of Computer and Software Engineering Information Technology University (ITU) Punjab Pakistan

ISBN: (纸本)9781837243235

In the era of social media, platforms have become integral to various domains, particularly business, where trends significantly influence decision-making processes. Despite numerous studies, effective decision-making in social networks remains a challenge. This study addresses these issues by proposing a novel model for analyzing decision-making strategies. A publicly available dataset containing social media user reviews of various products, including attributes such as identification, labels, country, and sentiment, is utilized. The dataset undergoes preprocessing and normalization, incorporating techniques such as tokenization, lemmatization, stop-word removal, and punctuation elimination. Deep learning methodologies are applied for model development and analysis, leveraging Python and the PyCharm framework. The proposed model is rigorously validated using state-of-the-art techniques and evaluated through extensive testing to measure its performance in terms of accuracy. Comparative analysis with recent methods underscores the effectiveness of the model. The findings of this study offer valuable insights for improving decision-making strategies, guiding new product development, and integrating diverse analytical models in social network contexts. © The Institution of Engineering & technology 2025.

关键词： big data Deep Learning sentiment analysis Social networks Twitter

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Design and Implementation of a Web-Based Application for Managing Agricultural Health Products 4

Design and Implementation of a Web-Based Application for Man...

引用

4th International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Oluwagbemi, Olugbenga O. Onyeka, Emebo Ishieka, Ikashi S. Wieclaw, Anna Azeta, Ambrose A. Middlesex University Department of Computer Science London United Kingdom Virginia Tech University Department of Computer Science Blacksburg United States Namibia University of Science and Technology Department of Software Engineering Windhoek Namibia

ISBN: (纸本)9798350353266

Caring for the health of food crops and plants can help boost food production and eradicate hunger according to the second United Nations (UN) Sustainable Development Goal (SDG). The procurement of agricultural health products such as fertilizers, fungicides, pesticides, and herbicides are a major problem for an average Nigerian farmer. This results in low crop yield and negatively impacts on food production which eventually causes hunger in the land. Existing methods of procuring these health products have majorly been through offline platforms such as agricultural stores and other relevant stores. This has its disadvantages such as inaccessible roads to stores where such products can be obtained, varying prices, health products out of stock, ignorance in the application of such products. These barriers deter farmers from procuring these health products. In this paper, a survey was conducted on the needs and problems of farmers. The results of the survey informed the design and implementation of an agricultural health Informatics web application for health product management for food crops. The application was designed using unified modelling language class diagram and implemented using Python. SQLite3 serves as the backend database. The agricultural informatics solution will help farmers in Nigeria to easily procure health products for food crops and get the products delivered without stress and worry when fully deployed. This will boost food production, thus eradicating hunger;it will also help in exporting food crops to other countries. © 2024 IEEE.

关键词： Agriculture AI Application Chatbot Farmers Health Informatics

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Double Encryption Technique for Sharing and Storing the Images in the Cloud Environment 4

Double Encryption Technique for Sharing and Storing the Imag...

引用

4th International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Anusha, P. Maruthi, R. Kumar, D. Maghesh Begum, H. Parveen Periyar Maniammai Institute of Science and Technology Department of Software Engineering Tamil Nadu Thanjavur India Hindustan Institute of Technology and Science Department of Computer Applications Tamil Nadu Chennai India

ISBN: (纸本)9798350343670

Cryptography is used by all organizations to protect the data files and ensures confidentiality mainly at the time of sharing and storing in the cloud data storage. The cloud service providers use a wide range of tools and techniques for encryption and storage technologies. The intruders are finding number of ways to break or attack the cryptographic systems, so that new ways are constantly being explored to safeguard our files from the attackers This paper explores double encryption cryptographic technique based on Elliptic curve cryptography (ECC) and Advanced Encryption Standard (AES) to perform end-to end encryption for sharing and storing files in the cloud environment. © 2024 IEEE.

关键词： Advanced Encryption Standard Cloud Cryptography Elliptic Curve Cryptography File Sharing

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Predictive Modeling for Early Identification of Disease Severity in Acute Respiratory Infections: A Case Study with COVID-19 5

Predictive Modeling for Early Identification of Disease Seve...

引用

5th International Conference on Advancements in Computing, ICAC 2023

作者： Kalapuge, Vindhya Kasthurirathna, Dharshana Sri Lanka Institute of Information Technology Department of Computer Science & Software Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

The COVID-19 pandemic, declared a global emergency by WHO in March 2020, has had a profound impact on global health. With millions affected, the pandemic's magnitude became evident. Even advanced healthcare systems grappled with resource shortages, from protective gear to ventilators. Late diagnosis and the inability to adapt to new SARS-CoV-2 variants led to misdiagnoses and high casualties. Overburdened healthcare facilities and the absence of a centralized patient data system hindered swift decision-making. The lack of real-time monitoring meant many didn't receive timely, personalized care, underscoring the need for an integrated healthcare response during global emergencies. To tackle these challenges and improve hospital resource management during pandemics, this study presents a web approach harnessing big data analytics to develop a machine learning predictive model. This model has been developed to enable hospitals to assess patients' severity in real-time, taking into account a wide range of factors such as symptoms, medical history, and demographics. To acquire data regularly, a streaming data pipeline was established using Apache Kafka and Apache Streaming. In predicting the severity levels of patients, both Multinomial Logistic Regression and Multilayer Perceptron (MLP) models were employed. One noteworthy finding was that using datasets with balanced classes yielded significantly higher accuracy for the MLP model compared to unbalanced datasets. Specifically, MLP achieved the highest accuracy of 60.4% when trained on balanced classes. This model's real-time severity insights have the potential to revolutionize decision-making and resource allocation in healthcare. It represents a significant step towards a more effective response to future pandemics and the strengthening of the global healthcare system. © 2023 IEEE.

关键词： COVID-19

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Stock Price Prediction using CuDNNLSTM and multiple CNN layers

Stock Price Prediction using CuDNNLSTM and multiple CNN laye...

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2023 IEEE International Conference on Artificial Intelligence, Blockchain, and Internet of Things, AIBThings 2023

作者： Kanwal, Anika Chandrasekaran, Siva Akhunzada, Adnan Swinburne University of Technology Department of Computer Science and Software Engineering Melbourne Australia University of Doha for Science and Technology Department of Computing and Information Technology Doha Qatar

ISBN: (纸本)9798350322347

Academic and financial sectors are interested in research areas that focus on understanding the patterns of financial activities and predicting their future changes. The daily movement of financial data involves complex, insufficient, and ambiguous information, making its prediction an extremely difficult endeavour. The issues of forecasting and analysing financial data is therefore complex and time-dependent. Deep neural networks (DNNs) can be used to solve nonlinear problems more effectively than conventional machine learning techniques. This study proposes an intelligent and optimal model CuDNNLSTM-Multi(1dCNN) for stock market price prediction using a hybrid of CUDA based Long Short Term Memory (LSTM) and Multiple One Dimensional Convolution Neural Networks (CNNs). A variety of models, including Multiple CuDNNLSTM-CNN, Multi-CuDNNLSTM, Multi (1dCNN), are used to compare the prediction performance of the proposed model. In terms of RMSE, MAE, and MAPE, the results show that the suggested model outperforms all hybrid and individual models. © 2023 IEEE.

关键词： Forecasting

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Intelligent Daycare: Enhancing Child Safety with IoT and Machine Learning Innovations 9

Intelligent Daycare: Enhancing Child Safety with IoT and Mac...

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

作者： Prathyanga, Amisha Shyaminda, Pasindu Chamikara, Pasan Lakshan, Sachin Thelijjagoda, Samantha Kasthurirathna, Dharshana Sri Lanka Institute of Information Technology Department of Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798350377972

Child safety and well-being in daycare settings are important, thus it is vital that the persons in charge be attentive to them. This research tries to solve the problem by introducing a smart and detailed monitoring system that utilizes new technology to enhance safety and support caretakers. The system has various innovative features, such as child missing detection, behavior and facial expression analysis, cry analysis and categorization, and fall detection. Using high-resolution cameras and on-device Edge AI capabilities, it ensures real-time surveillance of children's activities while protecting privacy. The approach integrates behavior and facial expression analysis to assess emotional states, cry recognition to discover childcare needs, as well as a useful fall detection system that can tell the difference between falls and other movements. All these parts synergize to hand over the necessary information to the caregivers on time and in a way that they can act upon. The quantitative experiments in real-time childcare centers show high accuracy through all the components, attesting to the system's reliability and durability. This research has important significance in improving the safety and the frequency response in daycare centers because it gives caregivers automated tools to quickly address the needs of children. While promising, the system has drawbacks, such as potential biases in data models and a reliance on ideal environmental circumstances. Future work intends to improve flexibility across varied daycare situations, improve accuracy through larger datasets, and investigate ethical frameworks for wider application. © 2024 IEEE.

关键词： Fall detection

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