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17th Asian conference on Intelligent information and Database Systems, ACIIDS 2025

17th Asian Conference on Intelligent Information and Databas...

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17th Asian conference on Intelligent information and Database Systems, ACIIDS 2025

ISBN: (纸本)9789819660070

The proceedings contain 29 papers. The special focus in this conference is on Intelligent information and Database Systems. The topics include: GAMER-Pong: Game Adjustment by Monitoring Emotional Response;Machine Learning Algorithms Comparison for Hand sEMG-Recorded Movements Classification;boosting Face Super-Resolution and Identification with Knowledge Distillation and Frequency Domain Loss;improving Efficiency Image Captioning by Using Attention Mechanism Combined with Knowledge Graph;integrating Topological Data Analysis and Deep Learning: A Case Study in Cardiovascular Disease Prediction at Thu Duc Hospital;boosting Lightweight Multi-branch Network by Integrating Segment, Skeleton, and Attribute information;continuous Recognition of Mouth Patterns in Japanese Sign Language for visual communication;Towards an UAV visual Navigation System Based on Map processing and Spatial Matching Techniques: A Literature Review;image Enhancement with Boosted Schrödinger Bridge;kernel-Level Energy-Efficient Neural Architecture Search for Tabular Dataset;estimation of Distribution Algorithms with Overlapped Subpopulations;energy-Efficient Edge Query processing for Smart City Using Query Prediction;influence Maximization with Fairness Cost on Groups in Online Social Networks;TMMP: Efficient Continual Learning via Mixture of LoRA Experts and Top Maximum Magnitude Parameter Fusion;ADMEdge: Accelerating Edge Computing Through Co-design for Enhanced Data Mobility;the Recursive Scheme of Clustering;TOPSIS-Inspired Socio-cognitive Mutation Operator for Metaheuristics;feature-Based Drift Detection in Non-stationary Data Streams Using Multiple Classifiers: A Comprehensive Analysis;a Feature Transformation Technique for Improving Ensemble Learning Systems;explainable Artificial Intelligence to Improve Interpretability in Predictive Mutation Testing;an Analysis of Class Imbalance Challenges in Predictive Mutation Testing;efficient Vietnamese Name Detection Using Highly Discriminative N-Gra

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Automated Classification of Snoring and Non-Snoring Sound Signals Based on Singular Superlet Transform

Automated Classification of Snoring and Non-Snoring Sound Si...

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International conference on Signal processing and communication (ICSC)

作者： Dhanhanjay Pachori Kamal Raj Tapan Kumar Jain Department of Electronics and Communication Engineering Indian Institute of Information Technology Nagpur India

ISBN: (数字)9798331511890

ISBN: (纸本)9798331511906

This paper proposes a new method for automated classification of snoring and non-snoring sound signals. The snoring and non-snoring sound signals have been represented using time-frequency representations (TFRs) obtained from singular superlet transform (SST). The obtained TFRs have been given as an input to various machine learning based classifiers, like convolutional neural network (CNN), visual geometry group 16-layer network (VGG-16), EfficientNet-B0, and hybrid VGG-16 models for the classification of snoring and non-snoring sound signals. From the experimental study, it has been observed that the hybrid VGG-16 with support vector machine (SVM) model has provided best performance among all the studied models with an accuracy, specificity, sensitivity, F1-score, and Matthews correlation coefficient (MCC) of 96.00%, 95.60%, 96.40%, 0.9602, 95.63%, and 0.9200 respectively. The good performance measures in automated classification of snoring and non-snoring sound signals show the effectiveness of the proposed method. The proposed method finds applications in the area of sleep disorder diagnosis and it is suitable for real-time implementation.

关键词： Support vector machines Time-frequency analysis visualization Accuracy Transforms Machine learning Signal processing Sleep apnea Convolutional neural networks Matrix converters

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mmHIU: a human-to-human interaction understanding system based on mmWave sensing

mmHIU: a human-to-human interaction understanding system bas...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Fenglin Zhang Chenglin Wu Anfu Zhou Huadong Ma School of Computer Science Beijing University of Posts and Telecommunications

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Human-to-human interaction understanding(HIU) plays a significant role in both physical and mental health of individuals in their daily lives. Nowadays, many of HIU tasks are based on visual information, which can compromise individuals’ privacy in daily life. In this paper, we propose mmHIU, a privacy-preserving HIU system based on mmWave sensing. To achieve mmHIU, we need to address two challenges: Extraction of interaction features and processing of raw data in a manner that preserves interaction features. We design multi-level feature extraction network mmHIU-STNet, and clustering-based frame normalization strategy to address these two challenges. We evaluate mmHIU on 10,811 point cloud sequences from 9 pairs of inter-actors, and results show that its accuracy reaches 90.56%, outperforming baseline methods, with a significant improvement in recognizing complex interaction behaviors.

关键词： Point cloud compression Privacy visualization Accuracy Neural networks Signal processing Feature extraction Sensors Millimeter wave communication Speech processing

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UAV-VLA: Vision-Language-Action System for Large Scale Aerial Mission Generation 25

UAV-VLA: Vision-Language-Action System for Large Scale Aeria...

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ACM/IEEE International conference on Human-Robot Interaction (HRI)

作者： Oleg Sautenkov Yasheerah Yaqoot Artem Lykov Muhammad Ahsan Mustafa Grik Tadevosyan Aibek Akhmetkazy Miguel Altamirano Cabrera Mikhail Martynov Sausar Karaf Dzmitry Tsetserukou Skoltech Moscow Russia

ISBN: (数字)9798350378931

ISBN: (纸本)9798350378948

The UAV-VLA (visual-Language-Action) system is a tool designed to facilitate communication with aerial robots. By integrating satellite imagery processing with the visual Language Model (VLM) and the powerful capabilities of GPT, UAV-VLA enables users to generate general flight paths-and-action plans through simple text requests. This system leverages the rich contextual information provided by satellite images, allowing for enhanced decision-making and mission planning. The combination of visual analysis by VLM and natural language processing by GPT can provide the user with the path-and-action set, making aerial operations more efficient and accessible. The newly developed method showed the difference in the length of the created trajectory in 22% and the mean error in finding the objects of interest on a map in 34.22 m by Euclidean distance in the K-Nearest Neighbors (KNN) approach. Additionally, the UAV-VLA system generates all flight plans in just 5 minutes and 24 seconds, making it 6.5 times faster than an experienced human operator. The code is available here: https://***/sautenich/uav-vla

关键词： visualization Navigation Human-robot interaction Nearest neighbor methods Autonomous aerial vehicles Natural language processing Satellite images Planning Trajectory Robots

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Towards an UAV visual Navigation System Based on Map processing and Spatial Matching Techniques: A Literature Review

Towards an UAV Visual Navigation System Based on Map Proce...

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17th Asian conference on Intelligent information and Database Systems, ACIIDS 2025

作者： Ramanauskas, Edvardas Jodlowiec, Marcin Krótkiewicz, Marek PIXPRO UAB Šiauliai Lithuania Faculty of Information and Communication Technology Wroclaw University of Science and Technology Wroclaw Poland School of Computer Science and Engineering International University Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Viet Nam

ISBN: (纸本)9789819660049

This paper reviews state-of-the-art methods for UAV navigation in GNSS-denied environments, focusing on map-based spatial matching and vision-based techniques. It identifies trends and challenges in current approaches, emphasizing the integration of cartographic data, visual odometry, and data fusion. A novel methodology for identifying research gaps is presented, combining systematic literature review, qualitative and quantitative analysis with multi-criteria evaluation. The findings highlight limitations in existing methods and propose a navigation system that leverages spatial pattern matching and object recognition, integrating vision-based and GIS data. This approach addresses real-time processing challenges and offers a foundation for improving UAV navigation accuracy in GNSS-denied settings. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Global positioning system

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Intelligent Computing System for Breast Cancer Identification Utilizing U-Net for Digital Image Segmentation

Intelligent Computing System for Breast Cancer Identificatio...

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Intelligent Data communication Technologies and Internet of Things (IDCIoT), International conference on

作者： Ranadheer Reddy Goli Vijaya Chandra Jadala Bhavana Jamalpur Department of Computer Science and Artificial Intelligene School of Computer Science and Artificial Intelligence SR University Warangal Telangana India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

As per WHO calculation of deaths, globally due to Breast Cancer is around one million. Breast cancer can be cured by early detection, in which abnormal breast cells proliferate uncontrollably, leading to tumor formation. Early detection of breast cancer may reduce deaths, as per World Health Organization records there are about one million deaths are caused by 2024 among them only 0.5 percent are of men. The different methodologies adopted in medical image processing related to breast cancer detection methods include mammography. We concentrated on medical image segmentation which helps to get visual information about breast tissue. This paper presents an intelligent computing system designed to support medical image processing through utilizing U-Net methodology for segmentation as part of deep learning methodologies. As part of Experimental Analysis, a BUSI dataset is used for the implementation of Deep Learning methodologies. A graphic analysis based on medical images, followed by an analysis of the results. This Intelligent Computing System based on artificial intelligence helps radiologists, medical professionals, and doctors with early and simplified breast cancer diagnosis.

关键词： Deep learning Image segmentation visualization Organizations Medical services Breast cancer Biomedical image processing Internet of Things Medical diagnostic imaging Tumors

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GRU Based Multiview Summarisation to obtain fast effective Key-frame

GRU Based Multiview Summarisation to obtain fast effective K...

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International conference on Computer, Electrical & communication Engineering (ICCECE)

作者： Vijay Anand Kandaswamy S Joshua Kumaresan Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences. Saveetha University Chennai India

ISBN: (数字)9798331513894

ISBN: (纸本)9798331513900

This paper proposes a novel approach to video summarisation by introducing a feature variance model that enhances video frame extraction. In typical video record aggregation systems, a variety of image processing tasks, such as acquisition, preprocessing, segmentation, and feature extraction, should be carried out with high precision to guarantee seamless system performance. Among these, feature extraction is essential since it breaks down large amounts of visual data into more manageable, informative vectors, and the success of the system is directly impacted by how effectively this process functions. Widely Recurrent Neural Networks (RNNs) are used in supervised video summarisation processes to obtain a model for the temporal dependencies between video frames. RNN’s ability to learn long-range memory in the video is limited, and CNN often focusses on local information while losing long-term knowledge when the input sequence is lengthy. Therefore, a Gated Recurrent Unit based Neural Network (GRU-NN) for video summarisation is proposed, to classify them into keyframes and non-keyframes. Keyframes are selected for summarisation, while non keyframes are discarded or reduced. We explore various keyframe extraction techniques, analyse the performance of the proposed summarisation model, and discuss the results. Finally, we offer insights into the model’s potential improvements and its broader applications in video summarisation tasks.

关键词： Deep learning Image segmentation Computer vision Analytical models Computational modeling visual analytics System performance Feature extraction Vectors Artificial intelligence

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Semantic Clustering of Image Retrieval Databases used for visual Localization

Semantic Clustering of Image Retrieval Databases used for Vi...

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IEEE Workshop on Applications of Computer Vision (WACV)

作者： Henry Hölzemann Torsten Fiolka Fraunhofer Institute for Communication Information Processing and Ergonomics FKIE

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Accurate self-localization of unmanned aerial systems (UAS) is needed to reduce their dependency on global navi-gation satellite systems (GNSS). Image retrieval techniques comparing aerial images with a reference database can be used for visual localization (VL). But the search space may be vast and a full search not feasible on a small UAS. In this work, we propose a novel solution that divides the reference database into smaller clusters based on the semantic con-tent of images. To this end, we generate and make use of a dataset for semantic segmentation of aerial image captures. By characterizing scenes and objects in images semanti-cally, retrieval-based systems are able to differentiate images and scenes efficiently. Using a divide-and-conquer approach, images with similar semantics are matched within smaller partial databases. This technique leads to reduced search times and approaches VL as a feasible solution for UAS localization in large-scale outdoor environments.

关键词： Location awareness Meters visualization Satellites Semantic segmentation Semantics Image retrieval Image capture visual databases Standards

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A Study on Smart Assistive Stick for visually Impaired Individuals

A Study on Smart Assistive Stick for Visually Impaired Indiv...

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visual Analytics and Data visualization (ICVADV), International conference on

作者： K.Y Megajai. P Nithish Kumar. K Lakshmi Prabha. Department of Advanced Communication Technology Chennai Institute Of Technology Chennai Tamil Nadu India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

Vision impairment heavily affects mobility, information access, employment, and independence for millions of people across the globe. Assistive technology for blind and visually impaired persons has grown from conventional white canes to high-tech smart devices with ultrasonic sensors, GPS, GSM modules, and artificial intelligence (AI). The current review compares development in smart assistive devices, such as smart blind sticks, wearable navigation systems, and IoT-based solutions. New technology like AI-based obstacle detection, image processing, and sensor fusion provide higher accuracy and realtime feedback. Despite this, prohibitive costs, short detection ranges, and issues in dynamic environments prevent its broad application. This research highlights major advancements, their drawbacks, and directions for future enhancement of accessibility, safety, and autonomy for the visually impaired. The suggested smart assistive stick prototype combines multi-sensor technology with AI-based capabilities to improve mobility and independence, overcoming current limitations in assistive navigation devices.

关键词： GSM Accuracy Navigation visual impairment Real-time systems Acoustics Sensor systems Artificial intelligence Intelligent sensors Global Positioning System

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Medical Image Noise Reduction Methods Based on Cutting-Edge Hybrid Filtering Designs

Medical Image Noise Reduction Methods Based on Cutting-Edge ...

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International conference on Smart Systems and Wireless communication, SSWC 2024

作者： Saha, Tanusree Vishal, Kumar Magadh University Bodh Gaya India Department of Information Technology JIS College of Engineering Kalyani India Department of Mathematics Magadh University Bodh Gaya India

ISBN: (纸本)9789819613472

A high-resolution noise free medical image is an essential requirement for accurate disease detection. Medical image processing and analysis techniques have become utmost important in recent decades as a supplementary competence for medical professionals in the fields of diagnosis and prevention. Obtaining high-quality images without any interference is a crucial requirement for effectively managing therapeutic images. Medical images may contain many types of noises for involvement of simultaneous operation of multiple equipment involved in data capturing and transmission. Various filters have been suggested for eliminating different types of noises, each having its own merits and drawbacks depending on its capabilities and projected benefits. Our research depicts a technique for removing noises from medical images using a hybrid filtering approach based on ensemble adaptive mean filtering and Block-matching-3D filtering. The proposed methodologies specifically aim to reduce the speckle noise in brain MRI images obtained from ultrasonic therapy by employing hybrid filtering techniques. This proposed hybrid filtering algorithms integrate the most effective aspects of high-performance filters to achieve a de-noised image that retains fine details, edges, and textures. The statistical performance measurement metrics like Root Mean Square Error (RMSE), Peak Signal-to-Noise Ratio (PSNR) are employed to evaluate the improved visual quality of the images. The results indicate that our proposed hybrid filtering technique functioning efficiently and provide better quality de-noised image compare with other existing de-noising techniques. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image enhancement

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