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Enhancing image Search Engine Accuracy Using Psychological Intention Diagrams 3

Enhancing Image Search Engine Accuracy Using Psychological I...

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3rd international conference on Integrated Circuits and Communication systems, ICICACS 2025

作者： Aswathi, K.P. Yadwad, Sunita Raj, D. Nirmal Rastogi, Shailesh Chennai Institute of Technology Department of Computer Science and Engineering Chennai India Parul University Faculty of Engineering and Technology Department of Computer science and Engineering Vadodara India Pune India Pune India

ISBN: (纸本)9798331508456

As the internet becomes increasingly image-centric, users face substantial challenges in efficiently locating desired images due to the inherent limitations of image search engines in interpreting visual content and keyword ambiguity. This paper introduces an innovative solution employing psychological intention diagrams to enhance search accuracy. These diagrams harness historical data to depict the search intentions associated with specific keywords, reflecting both user and web designer perspectives. By integrating these diagrams, our proposed search engine significantly refines its ability to predict and retrieve the most relevant images. The implementation of this system involves constructing two types of intention diagrams: one based on past user queries and another derived from the intentions of web designers for cases without historical user data. This dual approach not only captures the semantic essence of images more effectively but also addresses the multi-faceted meanings of search keywords. Our experimental results confirm that incorporating psychological dimensions into image search mechanisms substantially improves precision, facilitating more accurate and user-aligned image retrieval. This pioneering approach is the first to apply psychological profiling in enhancing image search engine functionalities, marking a significant advancement in digital image retrieval technologies. © 2025 IEEE.

关键词： Content based retrieval

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CGNet: Classification-Guided Multi-Task Interactive Network for Hyperspectral and Multispectral image Fusion

CGNet: Classification-Guided Multi-Task Interactive Network ...

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2025 IEEE international conference on Acoustics, Speech, and signal Processing, ICASSP 2025

作者： Lv, Guohua Xu, Yanlong Gao, Yongbiao Zhao, Guixin Wang, Guotao Sun, Xiangcheng Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China Shandong Key Laboratory of Ubiquitous Intelligent Computing Jinan China Ministry of Education China Beihang University Beijing China

ISBN: (纸本)9798350368741

The goal of fusing hyperspectral images (HSI) and multispectral images (MSI) is to generate high-resolution hyperspectral images for downstream tasks. However, most existing methods overlook the specific requirements of these tasks, leading to a gap between the fusion process and its subsequent applications due to insufficient guidance from downstream tasks. To address this issue, we propose a classification-guided multitask interactive network (CGNet) that integrates both fusion and classification tasks into a unified framework, with two branches producing the fused image and classification results, respectively. In the fusion branch, we design a multi-level residual refinement module to efficiently integrate spatial and spectral information. Additionally, an attention-based multi-scale fusion module, incorporating both spatial and channel attention, is carefully crafted to enhance representation learning. In the classification branch, both 2-D and 3-D convolutions are employed to improve classification performance. Moreover, an information interaction module is proposed to guide the fusion task based on classification outcomes. Extensive experiments demonstrate that our method outperforms state-of-the-art approaches on the Pavia Centre and Pavia University datasets. © 2025 IEEE.

关键词： Feature interaction Hyperspectral classification Hyperspectral image image fusion

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Automatic Tumour Segmentation Network based on PLHN Architecture and Dynamic Contrast-Enhanced Magnetic Resonance Imaging 3

Automatic Tumour Segmentation Network Based on PLHN Architec...

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3rd international conference on Integrated Circuits and Communication systems, ICICACS 2025

作者： Xie, Yaoxuan Li, Jiajian Rong, Chenyang Cheng, Yan College of Software Taiyuan University of Technology Taiyuan China

ISBN: (纸本)9798331508456

Tumor diagnosis often requires hemodynamic information to be judged. DCE-MRI technology can record and compare information well and has become an important tool in the diagnosis process. However, at this stage, the automatic segmentation technology for tumors is still not perfect, which is mainly due to the diversity of tumor morphology and signal intensity, and the insufficient performance of existing deep learning models in dealing with complex structures and image noise. In order to solve these problems, this paper proposes an automatic tumor segmentation method based on pyramid hierarchical network (PLHN) architecture and DCE-MRI. This method enhances the network's ability to recognize tumor boundaries and details through multi-scale feature extraction and hierarchical processing, and shows stronger robustness when dealing with dynamic changes in DCE-MRI. According to our experimental results on the data set, the method proposed in this paper can effectively improve the accuracy of tumor segmentation, which has strong research and practical significance. © 2025 IEEE.

关键词： image segmentation

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Unmasking Malware with MDCNN: A New Era of image-based Detection

Unmasking Malware with MDCNN: A New Era of Image-Based Detec...

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2025 international conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Gomathy, M. Vidhya, A. Tamilnadu Chennai India Department of Information Technology Tamilnadu Chennai India

ISBN: (纸本)9798331509828

Cybercrime has been a worldwide issue since the internet's inception. The various online crimes that take place have an impact on the general public. The various forms of cybercrime committed online and around the world are always evolving due to the development of the internet. In the current digital age, when malware diversity and volume are increasing rapidly, new methods must be used to identify malware more quickly and accurately. Malware, which includes worms, trojans, spyware, and adware, can have serious repercussions, including financial losses, data breaches, and the interruption of essential services. Manual heuristic inspection in malware analysis is neither efficient nor effective in keeping up with the rapid spread of malware or in analyzing new infections. Deep learning enhances automatic malware variant detection and classification as it provides better categorization by building neural networks with more potentially different layers. They have been applied to automate investigations using static and dynamic analysis, where malware with comparable behaviors is grouped and unidentified malware is categorized into families according to how close it is to the other malware. To analyze malware in a file, certain features from the PE header format may mislead the model into classifying malware as benign. However, when the same file is represented as an image, the distinct patterns in different sections make it easier to categorize the file as either malware or benign. Furthermore, as new malware variants emerge daily, the complexity of these patterns increases, making it difficult for traditional models to analyze them effectively. Our research proposes a deep learning model, MDCNN, to address the challenges faced by existing models in accurately categorizing executable files represented as images into malware or benign. This research study extracts various feature sets from malware image files, including patterns, sizes, and textures, we utilized a novel

关键词： Convolutional neural networks

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2nd international conference on Next-Generation Computing and Information systems, ICNGCIS 2023

2nd International Conference on Next-Generation Computing an...

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2nd international conference on Next-Generation Computing and Information systems, ICNGCIS 2023

ISBN: (纸本)9781032738659

The proceedings contain papers. The special focus in this conference is on . The topics include: Impact of artificial intelligence on high-penetration renewable physical infrastructure;collaborative attacks and defense;building a safe and secure metaverse;distributed-computing based versatile healthcare services framework for diagnostic markers;supervised context-aware Latent Dirichlet allocation-based drug recommendation model;combination kernel support vector machine based digital twin model for prediction of dyslexia in distributed environment;Time series analysis of vegetation change using remote sensing, GIS and FB prophet;advances in smart farming for precision agriculture: Green-IoT and machine learning as a solution;Towards a serverless computing and edge-intelligence architecture for the Personal-internet-of-Things (PIoT);IOT and developed deep learning based road accident detection system and societal knowledge management;Healthcare CAD model for hierarchical processing of surgical navigation system;applicability of eye tracking technology in virtual keyboard for human-computer interactions;a digital twin framework for smart contract-based DeFi applications in the metaverse: Towards interoperability, service scaleup & resilience;a novel approach to glass identification using ensemble learning for forensics;deep learning-based cognitive digital twin system for wrist pulse diagnostic and classification;Multi-class instance segmentation for the detection of cervical cancer cells using modified mask RCNN;dilated convolution model for lightweight neural network;artificial intelligence based Monte Carlo model for epidemic forecasting for societal aspect;efficient data visualization through novel recurrent neural network-based dimension reduction;An optimized CNN-based model for pneumonia detection;EEG-based emotion recognition: Leveraging CNNs for precision;A seven layer DNN approach for social-media multilevel image-based text classification.

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YOLOV9 Driven internet of Things Enabled Sustainable Solution for Intelligent Traffic Light Management System for Emergency Vehicles in Large Scale Urban Traffic 2

YOLOV9 Driven Internet of Things Enabled Sustainable Solutio...

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2nd international conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Vijay Ganesh, P. Sam Joshua, V. Saro Murugan, L. Mahalakshmi, S. Karpagam Academy of Higher Education Deemed to be University Department of Computer Science and Engineering Coimbatore India Karpagam Academy of Higher Education Deemed to be University Faculty of Engineering Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798331505745

Due to densely populated urban environment leads to huge traffic in peak hours, Intelligent traffic light management system becomes paramount for emergency vehicle transportation on leveraging the sensor technologies. However sensor data acquired from densely populated urban environment helps to process the traffic congestion based traffic density. Many researches has been carried out to enable intelligent transportation system using internet of things, artificial intelligence and communication technologies but still it requires sustainable solutions for intelligent transportation., traffic congestion management, traffic light controlling with respect to the detection of emergency vehicles like ambulance as it saves the life of the human being. In this paper, AI driven Intelligent of Things enabled sustainable solutions for intelligent traffic light management system for emergency vehicles in the large scale urban traffic. Initially sensor or camera deployed in the smart cities monitors the roads and its surroundings environments. Those acquired information is transmitted to the base station containing IoT servers. In IoT Server., video data is transformed into image frames and processed using YoloV9 based AI model. YoloV9 Model uses multiple component like backbone., neck and head for processing the image frame to recognize and tack the objects in each frame. Especially Backbone model employs convolution neural network for multi scale feature extraction and feature map generation on inclusion of the Generalized Efficient aggregation Network while neck component uses the path aggregation network for future fusion process and head component uses anchor box bounding box prediction method to detect and recognize the object of interest. On detect of the object of interest, distance and speed of the object is computed using gradient flow. Further model incorporates prediction approaches to detected emergency vehicle to estimate its speed and distance from traffic signal

关键词： Ambulances

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Interference Resisted Cross-dimensional Information Aggregation Local Refinement Network for Colposcopic image Lesion Segmentation 16

Interference Resisted Cross-dimensional Information Aggregat...

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16th international conference on signal Processing systems, ICSPS 2024

作者： Zheng, Zhipeng Huang, Guoheng Yang, Xiaohui Tan, Zhengguang Guangdong University of Technology China The Third Affiliated Hospital of Sun Yat-sen University China

ISBN: (纸本)9781510689251

In recent years, contour instance segmentation methods have rapidly developed due to advantages such as low computational costs, independence from detection boxes, and the absence of the need to process the internal pixel points of target objects. However, current contour instance segmentation methods still face issues like human-designed rough initial contours and overlapping contour vertex regression paths, which greatly limit the performance of contour instance segmentation algorithm models and increase the difficulty of learning for these models. Additionally, due to objective conditions such as high-light reflection areas and complex lesion edges in colposcopic images, existing models generally exhibit under-segmentation or over-segmentation phenomena. This paper proposes an end-to-end contour instance segmentation algorithm for colposcopic images, based on cross-dimensional attention mechanisms, autonomous initial contour generation, and multi-directional collaborative contour adjustment, effectively reducing the impact of the aforementioned issues and enhancing segmentation accuracy. On the cervical acetic acid map dataset provided by Dr. Yang Xiaohui from the Third Affiliated Hospital of Sun Yat-sen University, this paper's method has achieved improvements of 0.075, 0.047, and 0.013 in the Dice index, IoU index, and AP index respectively compared to the E2EC model. On the cervical iodine map dataset, the improvements in each index are 0.089, 0.079, and 0.069 respectively. © 2025 SPIE.

关键词： image segmentation

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A Cloud-Edge Integrated Water Extraction Using Superpixel Segmentation 18th

A Cloud-Edge Integrated Water Extraction Using Superpixel Se...

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18th international conference on Wireless Artificial Intelligent Computing systems and Applications, WASA 2024

作者： Zhang, Qingmin Chen, Chen Zhou, Yang Wang, Zhiyi Jin, Fan Lu, Haitao Wan, Shaohua School of Communication Engineering Xidian University Xi’an China Xidian Hangzhou Institute of Technology Hangzhou311231 China The State Grid Jilin Province Electric Power Company Limited Information Communication Company Changchun130000 China National Key Laboratory of Science and Technology on Space Microwave CAST Xi’an710100 China Ningbo Joynext Technology Corporation Ningbo361000 China The Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen518110 China

ISBN: (纸本)9783031714665

With the development of the Industrial internet of Things (IIoT) and the proposal of smart water conservancy, the integration of the internet of Things (IoT), edge computing, and computer vision for hydrological information monitoring has become a trend in water conservancy. Difficult to provide comprehensive hydrological data from hydrological observation stations, hydrological forecasting in uninformative basins has become one of the research hotspots. water extraction can provide support for many hydrological monitoring. However, The water is difficult to extract when the water in the near-water image has reflection and water surface ripples. We propose a multi-condition water extraction method based on superpixel segmentation under cloud-edge integration architecture. Firstly, based on the color characteristics of the water and the surrounding vegetation, we propose a reflection processing mechanism. Secondly, we propose a mechanism based on superpixel segmentation for the effect of reflection on water extraction. Then, we propose a contour correction mechanism. Finally, the flood-filling algorithm is used to extract the water. We compare the traditional image segmentation algorithm with our proposed segmentation method. The results show that the proposed method outperforms other algorithms in all evaluation metrics. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： image segmentation

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Artificial Intelligence-Powered internet of Things for Farming and Plant Research 3

Artificial Intelligence-Powered Internet of Things for Farmi...

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3rd international conference on Electronics and Renewable systems, ICEARS 2025

作者： Padma, A. Suresh, P. Narsimlu, G. Amarnath, M. Sreedhar, B.R. Chaitanya Bharathi Institute of Technology Department of Mathematics India

ISBN: (纸本)9798331509675

The wearable fiber optic sensors for plants to monitor microenvironment and growth wearable fiber optic plant sensors for microclimate and growth monitoring. The plant agricultural sectors are facing challenges because to population expansion and climate ***-powered computer vision-based plant growth monitoring system the goal of this research project is to use computer vision to create an AI-based plant growth monitoring system. The technology will allow for automatic and real-time evaluation of plant growth and health by utilizing computer vision algorithms and artificial intelligence approaches. Conventional techniques for tracking plant growth require a lot of work and time. By employing image processing techniques to analyze plant photos and extract pertinent features leaf area, plant height, and width, the suggested solution will overcome these drawbacks. Using the collected information, machine learning methods will be used to train models that can identify patterns and forecast plant development. The system will be made to adapt to various plant species and environmental circumstances. This AI-based plant growth monitoring system will help farmers, researchers, and plant enthusiasts by offering precise and timely insights into plant health and growth. This will allow them to make well-informed decisions about plant care and optimize growth conditions. By providing an economical, scalable, and effective system with numerous applications in horticulture, agriculture, and environmental research, this research project seeks to transform plant growth monitoring. © 2025 IEEE.

关键词： Smart agriculture

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Real-Time Dynamic image Stitching Using GNN-based Feature Matching 11

Real-Time Dynamic Image Stitching Using GNN-Based Feature Ma...

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11th international conference on Mechatronics and Robotics Engineering, ICMRE 2025

作者： Suh, Young Hoon Kim, Eun Ho Choi, Yeong Gwang Jeon, JaeWook Sungkyunkwan University Dept. of Electrical and Computer Engineering Suwon16419 Korea Republic of

ISBN: (纸本)9798331509293

Especially in static situations, image stitching has been routinely used to seamlessly merge many photographs into a panorama. Using it in dynamic and real-time environments, however, presents a number of difficulties, such as managing shifting objects, changing surroundings, and processing limitations. An image stitching look at that works well in dynamic real-time situations is presented in this paper. The method makes use of GNN-based feature matching, dynamic object removal, and real-time synthesis techniques. Comparative studies suggest that, in comparison to traditional methods like SIFT and ORB, the suggested approach meets real-time processing requirements while achieving greater registration precision, lower homography RMSE, and consistent panoramic outputs. The findings imply that the algorithm might be used in fields where precision and efficiency are required in dynamic circumstances, like parking management systems, sports broadcasting, and autonomous driving. © 2025 IEEE.

关键词： Photointerpretation

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