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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Nandi, Suman Maji, Giridhar Dutta, Animesh National Institute of Technology Dept. of Computer Science and Engineering West Bengal Durgapur India Asansol Polytechnic Dept. of Electrical Engineering West Bengal India

ISBN: (纸本)9798331531195

Exploring influential spreaders and predicting missing links in complex networks is essential for understanding and effectively controlling network dynamics. This paper presents a Graph Convolutional Network (GCN)-based link prediction method to estimate the probability of future link formation. We incorporate node features that capture local and global topological connectivity structures and feed these into the GCN model, where convolutional layers aggregate neighboring information and transform node features. This approach enables the model to capture structural patterns by integrating local and global information from neighboring nodes. In the final layer, the GCN model computes a prediction score representing the likelihood of an edge's existence, using insights gained during training. Finally, considering the predicted links, we update the network structure and introduce a novel centrality method called Emerging Spreader Centrality (ESC) to identify emerging spreaders within this augmented network. We conduct two separate experiments to evaluate the performance of the GCN-based link prediction and the ESC method, comparing their effectiveness with various state-of-the-art methods. Results demonstrate that our approach not only effectively predicts future links but also identifies emerging spreaders in the augmented networks. © 2025 IEEE.

关键词： Prediction models

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Cross-Domain Adaptation Through Adversarial Teaching for Unmanned Ariel Vehicles in Smart Cities 22

Cross-Domain Adaptation Through Adversarial Teaching for Unm...

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22nd International Learning and Technology Conference, L and T 2025

作者： Mazhar Ul Haq, Qazi Yao, Leethar Lin, Bo-Yu Yuan Ze University Dept. of IBPI and Computer Science and Engineering Zhongli Taiwan National Taipei University of Technology Dept. of Electrical Engineering Taipei Taiwan

ISBN: (纸本)9798331532215

Person re-identification has been an important issue of surveillance systems in smart cities. However, this requires huge datasets to supervise deep learning models for accurately identifying and tracking people in smart cities. In this paper, we propose adversarial learning in integration with a mutual mean teaching model known as adversarial teaching to take the issue of domain adaptation. Initially, they learn a source dataset in a supervised way. This model information is then transferred to another model through unsupervised learning and pseudo-labels are generated by generative adversarial learning. The multi-class invariance is handled by adversarial learning. The performance of the proposed model is evaluated on two different datasets source and target datasets. Multiple experiments on Duke and Market datasets indicate higher accuracy of F-1 score on both datasets compared to previous methods, demonstrating the superior performance of the proposed method. © 2025 IEEE.

关键词： Adversarial machine learning

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An Edge AI and Adaptive Embedded System Design for Agricultural Robotics Applications 25

An Edge AI and Adaptive Embedded System Design for Agricultu...

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30th Asia and South Pacific Design Automation Conference, ASP-DAC 2025

作者： Huang, Chun-Hsian Chen, Zhi-Rui Hsu, Huai-Shu Dept. Electrical Engineering National Changhua University of Education Changhua Taiwan Dept. Computer Science and Information Engineering National Taitung University Taitung Taiwan

ISBN: (纸本)9798400706356

This work presents the AgrBot, an agricultural robot designed to intelligently estimate and predict crop pest and disease severity (PDS). The AgrBot incorporates two binarized neural network (BNN) hardware modules for recognizing target crops and estimating their PDS. In a resource-constrained FPGA-based design, these BNN hardware modules can be configured on-demand, showcasing system adaptivity. Furthermore, a multimodal model that integrates crop images, sensor data, and time features is presented for predicting PDS. Employing edge artificial intelligence (AI) through the BNN hardware modules and the multimodal model enables the AgrBot to determine if biological agents are applied to protect crops from pests and diseases, creating a comprehensive agricultural cyber-physical system (CPS). Experimental results demonstrate accuracies of 76.3% for recognizing target crops, 65.3% for estimating PDS, and 67% for predicting PDS. In comparison to existing microprocessor-based design methods, the AgrBot's BNN hardware modules improve frames per second (FPS) by a factor of 790, while the multimodal model reduces processing time by up to 50.9%. © 2025 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Digital storage

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Designing and Implementing an IoT-Based Long-Distance Fire-Extinguishment System 'Stationary 3-Axis Fire-Emergency Response Base' 8

Designing and Implementing an IoT-Based Long-Distance Fire-E...

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8th International Conference on Electronics, Materials engineering and Nano-Technology, IEMENTech 2025

作者： Kundu, Shyantan Dey, Swarajit Abdullha, Md. Mustafa Sen, Gobinda Gangopadhyay, Malay Institute of Engineering & Management Dept. of Computer Science & Engineering Kolkata India Institute of Engineering & Management Dept. of Electronics & Communication Engineering Kolkata India

ISBN: (纸本)9798331520762

This paper presents the design and implementation of an Internet of Thing (IOT)-based long-distance fire-extinguishment system named 'Stationary 3-Axis Fire-Emergency Response Base' (STAFERB). The system aims to provide immediate response to fire hazards in various environments, including residential areas, building complexes, commercial areas, small-scale industries, large factories, dense slum areas, and remotely located forest regions. The proposed STAFERB is designed to receive fire location coordinates, fire intensity, and probable cause information. It then calculates the path of a projectile based on considering obstacles and self-detection of a clear line of sight to reach the t1arget location. The system uses this information to select the appropriate speed and type of fire retardant. The designed STAFERB features robust communication, self-path detection, auto-orientation, and reduced response time, making it a significant advancement in fire-fighting technology. Experimental results demonstrate the effectiveness of the proposed STAFERB in mitigating fire hazards and hence can be well suited for real-time disaster management. © 2025 IEEE.

关键词： Fire hazards

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Predicting the Likelihood of a Recurrent Febrile Seizure Using Thermographic Images and Deep Learning with Rules Generation 1

Predicting the Likelihood of a Recurrent Febrile Seizure Usi...

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1st International Conference on AIML-Applications for engineering and Technology, ICAET 2025

作者： Patil, Pratima Deshpande, Deepa S. MGM University Jawaharlal Nehru Engineering College Dept. of Computer Science and Engineering Chhatrapati Sambhajinagar431003 India

ISBN: (纸本)9798350355611

Recurrent febrile seizures are a common concern in children, often triggered by rapid changes in body temperature during fever episodes. For rapid action and care, early and accurate seizure recurrence prediction is crucial. Limited techniques were developed for predicting the likelihood of a Recurrent Febrile Seizure using a Thermographic image. Several drawbacks were considered for recurrent febrile seizure prediction such as lower performance, high time consumption, and error rate. Hence, the proposed technique is introduced to provide an efficient Deep Learning (DL)-based technique. Initially, the data is gathered from datasets, and the Intra-Attentional Convolutional ResNet (InAtt-CRNet) with the Yolov5-Face model detects the face and reduces the processing time by tacking the face with a bounding box. The patient's forehead or nose temperature is monitored using the Region of Interest (ROI) finding approach following face detection and tracking in each frame. Then, the temperature is predicted using the Enhanced Convolutional Capsule Network (EC2Net). The body temperature, age, duration of fever, and family history are used to construct the clinical decision rule. The Twin-directional Gated Long Short-term memory (TdG-LSTM) is used to predict the likelihood of a subsequent febrile seizure. The proposed technique achieves 98.16% accuracy, 98.45% precision, 0.12 RMSE, and 29 seconds as processing time. © 2025 IEEE.

关键词： Long short-term memory

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An optimal group-wise non-rigid image registration technique based clinical decision support for dementia prediction

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Multimedia Tools and Applications 2025年 1-26页

作者： Mary, F. Sheeja Asanambigai, V. Fred, A. Lenin Assistant Professor Senior Grade School of Computing Dept. of CSE Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Chennai India Computer Science and Engineering Annamalai University Chidambaram India Dept. of CSE Mar Ephraem College of Engineering and Technology Marthandam India

The fundamental challenge in clinical practice is predicting whether patients with cognitive symptoms or impairment will improve or stay stable. Population ageing is a worldwide phenomena with various impacts. Among dementia, Alzheimer disease (AD) is common in the elderly which is the most common form of dementia and may contribute to 60–70% of cases. An early and precise AD diagnosis enhances patient quality of life. In this study, we propose an optimal group-wise non-rigid image registration technique for dementia (AD) prediction. We introduce an improved cuckoo search optimization (ICSO) algorithm to search optimal group-wise dense correspondence in large image sets in non-rigid image registration which reduce dimensionality of the search space. Then, we illustrate a teacher-student learning based optimization algorithm which cooperates with PLS regression to extract deformation which separates the relationship information suitable for each other from the raw relationship information. After that, we compute the distribution of morphologies of age by fitting smooth percentile curves to these scores using hybrid capsule neural network with least Median Square (hybrid CNN-LMS) curve fitting model. Finally, the simulation results of proposed hybrid CNN-LMS technique are compared to the existing methods to analyze the measures. We observed that the accuracy of our proposed hybrid CNN-LMS technique is 96.23% and 95.82% for RSS and ANDI datasets respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Neurodegenerative diseases

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Multispectral Images and IoT Based Tea Plantation Monitoring: A Proposed System Architecture 5

Multispectral Images and IoT Based Tea Plantation Monitoring...

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5th International Conference on Advanced Research in Computing, ICARC 2025

作者： Kariyawasam, K.P.W.D.V. Kahandagamage, P.N. Fernando, M.R.R. Fernandopulle, J.M. Jayasinghearachchi, Vishan Dissanayaka, Kapila Dept. of Software Engineering Sri Lanka Dept. of Information Technology Sri Lanka Dept. of Computer Science Sri Lanka

ISBN: (纸本)9798331530983

In recent years, the Sri Lankan tea industry has fallen behind its competitors in the global tea market. This decline is caused by the challenges in productivity and resource management due to the limitations of traditional crop monitoring methods. This study presents a prototype system architecture that integrates multispectral imagery and IoT technologies to enhance plantation monitoring. The proposed system uses drones to capture high-resolution multispectral images and IoT devices to collect real-time environmental data, providing a comprehensive approach to assessing plant yield and detecting stress. Built on a hexagonal architecture, the system emphasizes modularity, reliability, and scalability by ensuring a clear separation of core functionalities from external components. This design facilitates easy adaptation to various crop types and agricultural contexts, enabling flexibility in deployment and use. This flexible framework can serve as a blueprint for improving decision-making and management practices across diverse plantation environments. © 2025 IEEE.

关键词： Risk perception

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Real-Time Traffic Prediction And Optimization using Machine Learning 3

Real-Time Traffic Prediction And Optimization using Machine ...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Vineetha, Kundrapu Tharun Reddy, M. Jaffer Ali, Md. Prathima, Narisetty Rajagopal, Shinu M. Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

The Real-Time Traffic Prediction and Optimization System may be described as enriched, spacious, algorithm-oriented, and designed to help contribute to the anti-symptomatic deprievement of increasingly congested urban traffic streams. The system with the help of integrating real time data of numerous sources like GPS tracker, traffic sensor as well as weather API, the system using advanced machine learning algorithm in order to predict traffic accurately and optimize routes based on it. This predictive capability then greatly improves the flow of vehicles, the time taken, and the occurrence of choke points. The structure of the system is fully based in the cloud, which makes it easy to scale and very compatible with other systems to amalgamate a vast array of data inputs with high dependability. Fully customizable and intuitive control panel gives live view of traffic, offers actionable stats, and instant control mechanisms to traffic operators to make correct decisions quickly. Congestion in urban areas has an influence on time taken to cover a certain distance, fuel consumption and emission to the atmosphere. On the basis of the above analysis, this work extends a Real-Time Traffic Prediction and Optimization System (RTPOPS) using machine learning approach to predict the traffic density and to further optimize traffic signals and routes. The used system demonstrated successful management and reduction of vehicle density by 30%, and increasing speed limits by 18% and proved that Intelligent Urban Mobility has the potential to be achieved. © 2025 IEEE.

关键词： Traffic congestion

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Exploring the Impact of Conditional Deep Convolutional Generative Adversarial Networks in Brain Tumor Image Classification: A Novel Approach

Exploring the Impact of Conditional Deep Convolutional Gener...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Upadhyay, Deepak Upadhyay, Abhay Sharma, Kuj Bihari Dhondiyal, Shiv Ashish Venu, Nookala Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era Deemed to Be University Dept. of Computer Science and Engineering Dehradun India Centre for Internet of Things Madhav Institute of Technology & Science Deemed University Gwalior India

ISBN: (纸本)9798331508685

In this paper, we explore the impact of conditional Deep Convolutional Generative Adversarial Networks (cDCGANs) with brain tumor image classification and introduce a new way to improve diagnosis accuracy. Shortage of Data/Data Imbalance: In Medical Imaging datasets, it helps in surging the data scarcity and class imbalance with mimicking generation images using cDCGAN. The detailed investigation demonstrates that synthetic data effectively improves classifier performance by acting as a form of augmentation, providing additional training examples for classifiers and serving as ground truth. From the computational efficiency analysis, we see that there is also scalability in training time with respect to dataset size. Consequently, this study presents cDCGANs as an important asset with which to improve diagnostic accuracy through brain tumor categorization in clinical contexts. © 2025 IEEE.

关键词： Generative adversarial networks

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Enhanced Image Captioning Using CNN and BLIP Models

Enhanced Image Captioning Using CNN and BLIP Models

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Tejesh, Vambara Supriya, M. Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331509828

This research focuses on generating image captions using Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) models. As deep learning advances, the availability of large datasets and increased computing power make it more feasible to build models capable of creating captions for images. This paper uses CNN and RNN models in Python to achieve this. Image captioning combines image recognition and Natural Language Processing (NLP) to interpret image context and express it in English, drawing on core computer vision principles. This study reviews important concepts in image captioning, including the applications of Keras, NumPy, and Jupyter notebook for development. This paper also explores the use of the Flickr dataset and CNN for image classification. The BLEU score of the proposed models is found to be close to 60%. On further enhancement to the model, it could be achieved to get a better score. © 2025 IEEE.

关键词： Convolutional neural networks

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