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5th International Conference on Advancements in Computing, ICAC 2023

作者： Alahakoon, Dulshan Samarakoon, Tharindu Wickramanayake, Indika Sakalasooriya, Kushani Kasthurirathna, Dharshana Karunasena, Anuradha Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350358131

Computers play a significant part in the modern world. The rapid increase in computer use, especially after the COVID-19 pandemic, raised concerns about the potential negative impact on user health. Sitting posture, eye health, stress, and sleep are the most important factors when considering the health of daily computer users. It is more important to prevent issues from occurring than to find solutions once they have occurred. To address this issue, a Real-Time Computer User Action Monitoring Approach is proposed that uses machine learning techniques to monitor and preserve user health effectively. Importantly, this solution leverages only the built-in webcam, eliminating the need for costly external equipment and ensuring affordability. The proposed solution consists of four key components: sitting posture detection and correction, eye tracking for eye health and comfort, stress detection and reduction techniques, and drowsiness detection for enhanced productivity and safety. This approach assists users in avoiding harmful behaviors, fosters healthful computer use by providing real-time feedback, and offers personalized recommendations. © 2023 IEEE.

关键词： Image processing

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Facial Skincare Product Suggestion with Product Popularity and Post Recommendation Care 4

Facial Skincare Product Suggestion with Product Popularity a...

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4th International Informatics and software engineering Conference, IISEC 2023

作者： Samarakoon, H.M.W.T.C.B. Ovitigala, A.U. Wijesinghe, G.A.T.T. Weerasinghe, K.M.L.P. Karunathilake, G.K.B.V. Weerathunga, Ishara Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350318036

Skin health significantly affects a person's overall wellbeing, which includes both physical and emotional aspects. The objective of this study is to develop an integrated system for recommending skincare products that integrates image processing, machine learning, sentiment analysis, and user feedback analysis to deliver individualized skincare advice and boost user satisfaction. Utilizing deep learning algorithms and face cues, the system uses user image analysis to detect important skin features, skin type, acne severity, and dark spots with extraordinary accuracy. Using sentiment analysis and clustering, the program identifies popular skincare products and tailors' recommendations to user preferences. Recommendations are further personalized by collaborative filtering. By using image analysis to identify allergies and other negative effects, user safety is increased. The technique prioritizes data safety and includes model training, sentiment analysis, image processing, and user input analysis. Initial findings demonstrate the excellent accuracy of skin issue detection and severity prediction. By allowing users from a variety of user demographics to make informed skincare decisions, this complete framework has the potential to change the skincare industry. © 2023 IEEE.

关键词： Sentiment analysis

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MHAN: Bottleneck Fusion Model Based on Hybrid Attention Network for Multimodal Emotion Recognition

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Journal of Shanghai Jiaotong University (Science) 2025年 1-8页

作者： Li, Kai Huang, Ya Zhong, Gang Nurmemet, Yolwas Wushouer, Silamu School of Software Xinjiang University Urumqi830091 China College of Information Science and Engineering Xinjiang University Urumqi830017 China Xinjiang Multilingual Information Technology Laboratory Xinjiang University Urumqi830017 China

Multimodal emotion recognition (MER) offers a more comprehensive and accurate understanding of human emotions by analyzing and integrating information from various modalities. However, previous work may have yet to fully consider the emotion feature information within and between modalities, which is the most significant challenge in this domain. Therefore, a novel multimodal hybrid attention network (MHAN) is proposed to achieve more effective information aggregation between text and audio. MHAN includes three main modules, i.e., hybrid encoder block (HEB), multi-head cross-attention (MCA) block, and bottleneck fusion (BF) block. Specifically, the HEB can extract advanced feature representation from text and audio sequences. Moreover, the MCA block can capture common features between text and audio to support emotion recognition. To filter out some redundant information, the BF block is designed to better extract the complementary and most relevant information between text and audio modalities. To validate the effectiveness of MHAN, we have conducted extensive experiments on the IEMOCAP and MELD datasets, and the experimental results demonstrate that MHAN outperforms existing state-of-the-art methods and better interprets subjective human emotions in the applications. (Figure presented.) © Shanghai Jiao Tong University 2025.

关键词： A bottleneck fusion (BF) multi-head cross-attention (MCA) multimodal emotion recognition (MER) multimodal hybrid attention network (MHAN) TP183

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An Improved Bi-LSTM EEG Emotion Recognition Algorithm

Journal of Network Intelligence

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Journal of Network Intelligence 2022年第3期7卷 623-639页

作者： Ma, Shuai Cui, Jianfeng Chen, Chin-Ling Xiao, Weidong Liu, Lijuan School of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China College of Software Engineering Xiamen University of Technology Xiamen361024 China Department of Computer Science and Information Engineering Chaoyang University of Technology Taichung41349 Taiwan

EEG emotion signal feature extraction is computationally intensive and the classification accuracy of the model is not high. Therefore, it can seriously affect the overall performance of classification algorithms. In order to better apply deep learning methods to EEG emotion recognition classification. To address the above problems, this paper uses the Fast Fourier Transformation algorithm to extract richer and more complete multi-channel feature information in feature extraction. At the same time, for the problem of low classification accuracy, an improved Bi-LSTM classification model is proposed. Finally, the model proposed in this paper is verified by 32-channel EEG signal data. The experimental results show that the classification method proposed improves the accuracy of Arousal to 93.35% and Valence to 93.09%. Compared with other models, the improved Bi-LSTM model has an excellent performance in EEG emotion recognition. Meanwhile, this paper explores the brain emotion mechanism by the accuracy of the improved model for different brain region channels, showing that the brain regions with the highest emotional relevance are the parietal and frontal lobes in the brain. © 2022 Taiwan Ubiquitous information CO LTD. All rights reserved.

关键词： Emotion Recognition

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Web Application to predict Skin Disease: An Approach Using Deep Learning Algorithm 6

Web Application to predict Skin Disease: An Approach Using D...

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

作者： Thayalan, Eelanila Arumugavel, Ayingaran Ganeshan, Sharujan Dalaxshan, Makenthiran Fernando, Harinda Rajapakse, Kaushalya Department of Information Technology Faculty of computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Systems Engineering Faculty of computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Science and Software Engineering Faculty of computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350394634

The Disease Prediction System revolutionizes healthcare with advanced machine learning techniques for early detection of skin diseases, notably focusing on skin cancer. Through image processing and Transfer Learning, it excels in identifying potential malignancies, enabling timely interventions. Its capabilities extend to benign tumors and growth disorders, utilizing the ResNet50 Model for precise identification. Additionally, it predicts skin reactions associated with various dermatological conditions, such as Urticaria Hives and Warts, leveraging the efficientnetB0 Model. Integration of the VGG16 Model enhances diagnostic accuracy for inflammatory skin conditions. This holistic approach prioritizes patient-centric care, leveraging diverse datasets and intricate pattern recognition in medical images. The system's proactive nature embodies personalized solutions for early detection, timely intervention, and improved patient outcomes. Its versatility and accuracy underscore itstransformative potential in healthcare delivery. By harnessing diverse datasets and recognizing intricate patterns within medical images, it heralds a new era of personalized healthcare solutions. In essence, the Disease Prediction System exemplifies the transformative potential of machine learning in healthcare, ensuring the highest standards of diagnostic accuracy and efficacy, while prioritizing patient well-being and quality oflife. © 2024 IEEE.

关键词： Deep learning

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Accurate Stress Detection for Developers: Leveraging Low-Cost IoT Devices (ESP32 and MAX30102) to Analyze Heart Rate Variability via an External Mouse 5

Accurate Stress Detection for Developers: Leveraging Low-Cos...

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Jayathilake, Amantha Ranasinghe, Janudi Perera, Dulshan Bartholomeusz, Shehan Rathnayake, Samadhi Ganegoda, Devanshi Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Colombo Sri Lanka Sri Lanka Institute of Information Technology Department of Information Technology Colombo Sri Lanka

ISBN: (纸本)9798350358131

In today's fast-paced work environment, stress levels have become a critical concern. This research introduces an innovative approach to address this issue by utilizing readily available, cost-effective components - the ESP32 microcontroller and the MAX30102 heart rate sensor, seamlessly integrated into an external mouse. This non-intrusive system enables real-time stress assessment for developers during their tasks. By analyzing heart rate variability data, a practical and effective solution is offered. The collected data is securely transmitted for analysis, potentially revolutionizing developer well-being and productivity. This study pioneers a unique and practical stress detection solution finely tuned to the software development milieu. © 2023 IEEE.

关键词： Microcontrollers

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Design of Fixed-point FFT Algorithm Based on Memristor 4

Design of Fixed-point FFT Algorithm Based on Memristor

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4th International Conference on Computers and Artificial Intelligence technology, CAIT 2023

作者： Zhou, Zhexian Zhong, Xuefeng Yu, Yongbin Wang, Xiangxiang Feng, Xiao Shen, Jiarun Tashi, Nyima University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu China Tibet University School of Information Science and Technology Lhasa China

ISBN: (纸本)9798350326710

This study introduces a novel approach, leveraging memristor-based fixed-point computing, to implement a high-performance Fast Fourier Transform (FFT) algorithm. Memristors enable direct data processing within memory, addressing power inefficiencies and processing bottlenecks associated with conventional architectures. The proposed method emphasizes parallelism through memristor calculations, enhancing computational efficiency in resource-constrained environments. In the image processing and restoration algorithms, the Memristor-based Fixed-point FFT demonstrates both efficacy and accuracy. Despite precision constraints in memristor-based fixed-point FFT, normalization strategies effectively mitigate noise in image transformations. © 2023 IEEE.

关键词： Memristors

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Research and optimization of meteorological big data storage technology

Research and optimization of meteorological big data storage...

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2022 International Conference on Electronic information engineering and Computer Science, EIECS 2022

作者： Zhang, Shiyang Xie, Hansheng Luo, Fei Zhang, Xi College of Software Engineering Chengdu University of Information Technology Chengdu China Hainan Provincial Meteorological Information Center Hainan China

ISBN: (纸本)9781510663312

With the rapid development of the Internet, data analysis and other information fields, the amount of meteorological data is increasing exponentially every year, and the types of meteorological data are increasing. The storage of meteorological data should not only reach the response speed of millisecond level, but also provide data products and services in a short time. By analyzing the characteristics of meteorological data structure, this paper puts forward different storage methods for different data types. According to the data requirements of the meteorological business operations, this paper proposes the use of hierarchical storage, to store frequently used or soon-to-be-used data stored in high-performance memory. At the same time, a record table based on file access characteristics is established to record data attributes, and quickly locate the data that needs to be migrated. By simulating the data response efficiency of querying historical similar weather, and comparing the performance of the original single storage mode with the optimized storage mode, it is verified that hierarchical storage can improve the demand for data response of meteorological services. © 2023 SPIE.

关键词： Big data

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U-net of joint spatial domains with multi-scale atrous convolution for rectal image segmentation

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Multimedia Tools and Applications 2025年 1-17页

作者： Rao, Yunbo Gao, Li Zeng, Shaoning Shao, Tingting Sun, Jihong School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu611731 China Yangtze Delta Region Institute University of Electronic Science and Technology of China Huzhou313000 China Zhejiang University School of Medicine Zhejiang Hangzhou310058 China

Medical image segmentation is very important for the diagnosis of related diseases. To reduce the labeling work of related medical images, numerous models based on U-Net have been proposed to achieve automatic segmentation of target regions. However, most of these models are only trained in one coordinate system, ignoring the joint effects of different spatial coordinate systems. In addition, most of the encoding modules in these models do not pay attention to multi-scale spatial information. Our proposed solution for the aforementioned challenges involves using U-Net model with joint spatial domains and multi-scale encoding module, which enables us to segment rectal image better. The model includes a self-designed multi-layer dilated convolution encoding module named AIR (Atrous Inception Residual Block), to achieve a multi-scale content fusion. Besides this, it utilizes the center point and polar coordinates to realize attention mechanism and rotation invariance. Furthermore, retraining the output of polar coordinate network with Cartesian coordinate system realizes the translation invariance of segmentation. Compared with the commonly used medical segmentation models, the dice coefficient of our model is improved by about 2% on our in-house rectal dataset. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Image segmentation

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Development of a Prediction System for Criminality and Suicidal in Sri Lanka 5

Development of a Prediction System for Criminality and Suici...

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Ranasinghe, D.P.D. Fernando, R.D.T.S. Girambe, D.G. Arambegama, D.M.H.K.D. Chathurika, Bhagyanie Jayasinghe, Dulani Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350358131

This research confronts the growing challenges of rising suicide rates and crime in Sri Lanka through a dual approach, harnessing advanced machine learning and time series analysis. The innovative methodology, merging 'Feature Importance Weighting and Ensemble of Diverse Trees with the Random Forest Regressor,' achieves impressive accuracy of 93% for factors and 94% for methods. Additionally, the accuracy rates of 94% for education and 93% for occupationbased predictions enhance this comprehensive approach, offering a profound understanding of suicidal incidents and enabling precise prevention strategies. Simultaneously this study employs an ensemble of AutoRegressive Integrated Moving Average (ARIMA) models for crime prediction. This approach consistently attains high accuracy, often surpassing 90%, such as 96% and 97%. Utilizing multiple models enhances predictive accuracy and hotspot identification, granting law enforcement and policymakers flexibility to address enduring yearly hotspots and evolving dynamic hotspots. This research bridges the comprehension of suicide and crime dynamics in Sri Lanka, providing invaluable insights for evidence-based policies and targeted interventions. © 2023 IEEE.

关键词： Forecasting

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