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5th International Symposium on Signal and Image Processing, ISSIP 2024

作者： Chatterjee, Soham Gupta, Ritwika Das Latifi, Shpetim Behera, Tapan Kumar Department of Statistics and Data Science CHRIST Deemed to be University Bangalore India Department of Computer Science AAB College Pristina Kosovo Technical Architect Independent Researcher BostonMA United States

ISBN: (纸本)9789819795147

This study offers a novel method for developing classification approaches for disease prediction. Exploratory analysis and meticulous data preprocessing were conducted to understand the relationships between symptoms and illnesses. The research involved assessing various machine learning models, including the random forest classifier, through cross-validation techniques, resulting in the identification of a high-performing model with an impressive accuracy rate. In addition, this study incorporates data visualization techniques to gain insights into symptom–disease connections. The study’s focus on data visualization and optimization strategies in health demonstrates the potential to transform healthcare by providing precise diagnoses and predicting diseases, ultimately improving patient outcomes. This research underscores the efficacy of data-driven techniques and their integration into recommendation and disease prediction systems, emphasizing the significance of data visualization and optimization strategies in health. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Patient treatment

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SDCG: Silhouette-based Deep Clustering with GNN for Improved Graph Node Clustering 21

SDCG: Silhouette-based Deep Clustering with GNN for Improved...

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21st IEEE/ACIS International Conference on Software Engineering Research, Management and Applications, SERA 2023

作者： Shin, Hyesoo Jang, Eunjo Kim, Sojeong Lee, Ki Yong Sookmyung Women's University Computer Science and Data Intelligence Lab Seoul Korea Republic of

ISBN: (纸本)9798350345889

Graph Neural Networks (GNNs) are powerful tools for analyzing graph-structured data in various fields because of their great expressive power for graph data. They use a message-passing mechanism to update node embeddings, which are then used for tasks such as node classification and link prediction. Recently, node embeddings have also been used in research on graph node clustering, which aims to group similar nodes based on their features and graph topology. However, traditional methods for node clustering have a limitation in that GNNs only focus on generating node embeddings without considering the ultimate objective of clustering. To address this issue, a novel technique called "Deep Clustering"has been proposed, which integrates both node embedding and clustering stages. This requires defining a new loss function by simultaneously minimizing the GNN loss and the clustering loss. Our proposed loss function incorporates not only the distance within clusters but also the distance between clusters by applying the Silhouette coefficient, which enables us to achieve better clustering results. In this paper, we propose a Silhouette-based Deep Clustering with GNN (SDCG) to more effectively cluster nodes in a graph by iteratively training the embedding model to produce embedding vectors with improved clustering results. Through extensive experiments, we demonstrate that SDCG outperforms the conventional approach of performing embedding and clustering independently. © 2023 IEEE.

关键词： Graph neural networks

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Unveiling Patterns, Visualizations, and Trends from Patient Diabetes data 5th

Unveiling Patterns, Visualizations, and Trends from Patient ...

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5th International Symposium on Signal and Image Processing, ISSIP 2024

作者： Gupta, Ritwika Das Chatterjee, Soham Latifi, Shpetim Behera, Tapan Kumar Department of Statistics and Data Science CHRIST Deemed to be University Bangalore India Department of Computer Science AAB College Pristina Kosovo Technical Architect Independent Researcher BostonMA United States

ISBN: (纸本)9789819795147

The important role that exploratory data analysis, or EDA, plays in the context of diabetes prediction is explored in this work. EDA is used as a key component of a multimodal strategy to identify unique characteristics linked to diabetes. EDA offers insights that aid in the creation of prediction models by sifting through the complex patterns present in the medical data. The focus is on using EDA to fully grasp the data landscape while also comprehending the distinct features of diabetes. This investigation is critical to accurately categorizing people into discrete risk groups and emphasizes the use of domain-specific knowledge in enhancing diabetes prediction techniques. The research suggests using specific EDA techniques to gain deep insights and lead proactive responses. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Biomedical engineering

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Air quality and dust level monitoring systems in hospitals using IoT

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Discover Internet of Things 2025年第1期5卷 1-18页

作者： Parkavi, A. Sowmya, B.J. Alex, Sini Anna Supreeth, S. Shruthi, G. Rohith, S. Chatterjee, Sudipta Lingaraj, K. Department of Computer Science and Engineering Ramaiah Institute of Technology Bengaluru560054 India Department of Artificial Intelligence and Data Science Ramaiah Institute of Technology Bengaluru560054 India Ramaiah Institute of Technology Bengaluru560054 India School of Computer Science and Engineering REVA University Bengaluru560064 India Department of Electronics and Communication Engineering Nagarjuna College of Engineering and Technology Bengaluru562164 India Department of Electronics and Communication Engineering Brainware University Barasat West Bengal Kolkata700125 India Department of Computer Science and Engineering Rao Bahadur Y. Mahabaleswarappa Engineering College Bellary583104 India

This system provides a comprehensive overview of hospital environments by tracking air quality, dust, temperature, and humidity simultaneously, offering a more complete picture of indoor conditions than systems that focus on fewer parameters. The use of IoT technology enables real-time monitoring and alerts, allowing hospital staff to respond immediately to any issues. Additionally, predictive analytics anticipate future changes, giving staff time to take preventive measures. The integration with a mobile app and cloud-based data storage ensures easy access to data, convenient monitoring, and the ability to scale the system across multiple hospital facilities, catering specifically to the unique needs of different healthcare environments. © The Author(s) 2025.

关键词： Indoor air pollution

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Image Caption Generation using CNN and Audio Conversion 2

Image Caption Generation using CNN and Audio Conversion

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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Priya, K. Babu, R.Vijay Reddy, M.Muralidhar Mohan Reddy, T.H. Maanesh, M. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Artificial Intelligence & Data Science Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science & Engineering Chennai India Arizona state university Department of Data Science Tempe United States

ISBN: (纸本)9798350368413

This research focuses on developing a system that can generate descriptive captions for images and convert them into audio output. The system employs a Convolutional Neural Network (CNN) to extract visual features from images, which are then fed into a Gated Recurrent Unit (GRU) to generate textual descriptions. The generated captions are subsequently converted into audio using text-to-speech techniques. By training the model on a large dataset of image-caption pairs, the system learns to associate visual information with textual descriptions, enabling accurate and coherent caption generation. This research contributes to the advancement of image understanding and generation, with potential applications in various fields such as image search, accessibility, and content creation. © 2024 IEEE.

关键词： Convolutional neural networks

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Subgraph-Aware Training of Language Models for Knowledge Graph Completion Using Structure-Aware Contrastive Learning 25

Subgraph-Aware Training of Language Models for Knowledge Gra...

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34th ACM Web Conference, WWW 2025

作者： Ko, Youmin Yang, Hyemin Kim, Taeuk Kim, Hyunjoon Department of Artificial Intelligence Hanyang University Seoul Korea Republic of Department of Data Science Hanyang University Seoul Korea Republic of Department of Computer Science Hanyang University Seoul Korea Republic of

ISBN: (纸本)9798400712746

Fine-tuning pre-trained language models (PLMs) has recently shown a potential to improve knowledge graph completion (KGC). However, most PLM-based methods focus solely on encoding textual information, neglecting the long-tailed nature of knowledge graphs and their various topological structures, e.g., subgraphs, shortest paths, and degrees. We claim that this is a major obstacle to achieving higher accuracy of PLMs for KGC. To this end, we propose a Subgraph-Aware Training framework for KGC (SATKGC) with two ideas: (i) subgraph-aware mini-batching to encourage hard negative sampling and to mitigate an imbalance in the frequency of entity occurrences during training, and (ii) new contrastive learning to focus more on harder in-batch negative triples and harder positive triples in terms of the structural properties of the knowledge graph. To the best of our knowledge, this is the first study to comprehensively incorporate the structural inductive bias of the knowledge graph into fine-tuning PLMs. Extensive experiments on three KGC benchmarks demonstrate the superiority of SATKGC. Our code is available. © 2025 Copyright held by the owner/author(s).

关键词： contrastive learning

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Boli: A dataset for understanding stuttering experience and analyzing stuttered speech

Boli: A dataset for understanding stuttering experience and ...

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

作者： Batra, Ashita Narang, Mannas Sharma, Neeraj Kumar Das, Pradip K. Department of Computer Science and Engineering Indian Institute of Technology Guwahati Guwahati781039 India Mehta Family School of Data Science and AI Indian Institute of Technology Guwahati Guwahati781039 India

ISBN: (纸本)9798350368741

There is a growing need for diverse, high-quality stuttered speech data, particularly in the context of Indian languages. This paper introduces Project Boli, a multi-lingual stuttered speech dataset designed to advance scientific understanding and technology development for individuals who stutter, particularly in India. The dataset constitutes (a) anonymized metadata (gender, age, country, mother tongue) and responses to a questionnaire about how stuttering affects their daily lives, (b) captures both read speech (using the Rainbow Passage) and spontaneous speech (through image description tasks) for each participant and (c) includes detailed annotations of five stutter types: blocks, prolongations, interjections, sound repetitions and word repetitions. We present a comprehensive analysis of the dataset, including the data collection procedure, experience summarization of people who stutter, severity assessment of stuttering events and technical validation of the collected data. The dataset is released as an open access to further speech technology development. © 2025 IEEE.

关键词： Indian stuttered speech dataset Intelligibility assessment read speech spontaneous speech Stuttering event detection

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Prediction of Word Game Trends and Human Behavior Based on ARIMA and BP Neural Networks

Prediction of Word Game Trends and Human Behavior Based on A...

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2023 International Conference on Artificial Intelligence, Systems and Network Security, AISNS 2023

作者： Wu, Rongbang Ye, Zhijie Ni, Mo Fuzhou University College of Computer and Data Science College of Software Fujian Fuzhou350108 China

ISBN: (纸本)9798400716966

As an emerging word puzzle game provided by New York Times daily, Wordle attracts everyone worldwide because the rules are simple, and the game is specifically designed for relaxing in fragmented time. This study aims to enhance the Wordle game experience by analyzing historical data, predicting future gamer numbers using double ARIMA that improves upon traditional ARIMA, and exploring word attributes. We investigated the impact of word frequency and the number of repeated letters on puzzle difficulty, revealing a correlation through Pearson's coefficient test. Utilizing a BP neural network, we predicted word difficulty and optimized K-means clustering for a comprehensive analysis. Additionally, we discovered a relationship between gamer numbers and special days, conducting sensitivity analysis to refine our model. Our study contributes by pioneering the double ARIMA approach, refining clustering methods, inferring word difficulty mathematically, and uncovering hidden insights in human behavior through multidimensional data analysis. © 2023 ACM.

关键词： K-means clustering

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LGMD-based Neural Network for Detecting Abnormal Velocity Targets in Moving Crowd 9

LGMD-based Neural Network for Detecting Abnormal Velocity Ta...

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9th International Symposium on computer and Information Processing Technology, ISCIPT 2024

作者： Zhao, Zhongxiang Hu, Bin Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang550025 China Guizhou University College of Computer Science & Technology Department of Computer Science Guiyang550025 China Artificial Intelligence Research Institute of Guizhou University Guizhou University Guiyang550025 China

ISBN: (纸本)9798350388404

Detecting abnormal velocity targets is critical especially in public surveillance for crowd activity monitoring. Although there are some explorations on one such issue, traditional methods still perform poorly in complex scenes. In this paper, a novel visual neural network is investigated to perceive abnormal velocity targets in moving crowd, based on the latest neurophysiological achievements revealed in locusts' vision systems. The proposed neural network contains two neural counterparts, i.e., the presynaptic and the postsynaptic networks. The former one receives visual signals and processes them to capture the motion cues of different targets, and the latter one filters the salience energies to perceive abnormal velocity targets in the field of view. Numerical experiments carried out show that the proposed neural network can effectively detect abnormal velocity targets in moving crowd. This study is an important step towards dynamic visual information processing in crowd behavior analysis. © 2024 IEEE.

关键词： Visual servoing

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Exploring the Impacts of AI Technologies on Digital Collage Creation

Exploring the Impacts of AI Technologies on Digital Collage ...

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2025 CHI Conference on Human Factors in Computing Systems, CHI EA 2025

作者： Luo, Yalong Wu, Leyan Huang, Yirui Cai, Wanling Jin, Yucheng Duke Kunshan University Suzhou China School of Computer Science and Statistics Lero @ Trinity College Dublin Dublin Ireland Data Science Research Center Duke Kunshan University Suzhou China

ISBN: (纸本)9798400713958

AI technology has been increasingly integrated into digital art-making tools to support artistic creation, such as automatically colorizing sketches. Although research on the application of AI in art and creative practices is growing, its role in collage creation has seen limited research attention. To explore the potential of AI in digital collage creation, we followed a human-centered approach and conducted a co-design workshop with eight participants. Through this workshop, we examined how users interact with AI during the creation process, explored the impact of AI on their creation process and experience, and investigated their attitudes toward AI-assisted creation. Our findings offer valuable design implications for the future development of AI-assisted digital collage tools. © 2025 Copyright held by the owner/author(s).

关键词： User centered design

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