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18th International Joint Conference on Biomedical engineering Systems and Technologies, BIOSTEC 2025

作者： Marczyk, Michal Wrobel, Agata Merta, Julia Polanska, Joanna Department of Data Science and Engineering Silesian University of Technology Gliwice44-100 Poland Yale Cancer Center Yale School of Medicine New HavenCT06511 United States Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice44-100 Poland

Digital pathology allows for the efficient storage and advanced computational analysis of stained histopathological slides of various tissues. Tissue segmentation is a crucial first step of digital pathology aimed at eliminating background, pen markings, and other artifacts, reducing image size, and increasing the efficiency of further analysis. In most cases, color thresholding or deep learning models are used, but their effectiveness is reduced due to complex artifacts and huge color variations between slides. We propose a post processing method to increase the tissue segmentation performance of any initial segmentation algorithm. Using a set of 197 manually annotated histopathological images of breast cancer patients and 63 images of endometrial cancer patients, we tested our method with 3 thresholding techniques and 3 deep learning-based algorithms by calculating the Dice index, Jaccard index, precision, and recall. In both datasets, applying post processing increased precision and recall for thresholding methods and mostly precision for deep learning models. Overall, applying post-processing gave better tissue segmentation performance than initial segmentation methods, significantly increasing Dice and Jaccard indices. Our results proved that thanks to post-processing, the tissue segmentation pipeline is more robust to noises and artifacts commonly present in histopathological images. © 2025 by SCITEPRESS– Science and Technology Publications, Lda.

关键词： Image segmentation

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HerbSimNet: Deep Learning -Based Classification of Indian Medicinal Plants with High Inter-Class Similarities

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Procedia computer Science 2025年 258卷 765-774页

作者： N. Shobha Rani Bhavya K R Pushpa B.R. Ragavendra M. Devadas Department of Artificial Intelligence and Data Science GITAM School of Technology Bengaluru GITAM (Deemed to be) University India Department of Computer Science and Engineering GITAM School of Technology Bengaluru GITAM (Deemed to be) University India Department of Computer Science School of Computing Mysuru Amrita Vishwa Vidyapeetham India Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India

Medicinal plant species recognition is important across diverse sectors such as Ayurveda, agriculture, environment conservation and botanical research. Specific groups of plants in Indian medicinal plant ecosystem exhibit significant inter-class similarities due to varying abundance and ecological factors. To address the challenges involved in the process of classifying these species in this work a deep learning model Herb-SimNet is proposed. The Herb-SimNet analyzes similarity of plant species over other plant species using vision based deep learning and machine learning techniques. The proposed model works based on the combination of wavelet features and convolutional features extracted using three sequential convolution layers to extract the prominent features that distinguish variations among the inter class similarity plant species. To perform experiments, a dataset is created by capturing medicinal plant leaf images using box model in plain background and uniform lighting. A smart phone captured twelve Indian medicinal plant species comprising of about 1400+ samples that belongs different plant species but similar morphological structure is collected. Baseline experiments are carried out between Herb-SimNet and other state-of-the-art deep learning models for classification based on the proposed dataset. The outcomes demonstrate that Herb_SimNet provides clear interpretation one plant variety with others and achieves superior accuracy in prediction than that of state-of-the-art approaches. Furthermore, the model demonstrates better generalization towards the other inter-class similarity groups considered for testing. In conclusion, the proposed dataset and Herb-SimNet plays a a crucial role in advancement of research concerning Indian medicinal plant species classification resulting into enhancement of AI-based technology for biodiversity conservation and ethnobotanical studies.

关键词： Classification inter-class similarities confusing plant species medicinal plants wavelet image features

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Development of Fast Prediction System of User Action for a Fully Autonomous Smart Home System 4

Development of Fast Prediction System of User Action for a F...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Rajalakshmi, N.R. Sharmila, L. Prabu, S. Veeramakali, T. Arulkumaran, G. Chourasia, Pranshu Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu India School of Computing Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Chennai India School of Computing Srm Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur Chennai India School of Computing and Information Technology Reva University Karnataka Bangalore560064 India

ISBN: (纸本)9798331523923

The HVAC system, energy storage building, distributed power supply, and other equipment are integrated into the scheduling algorithm, which is aimed at reducing household electricity consumption. It is also assumed that users can provide energy to the grid according to their own conditions. Taking electricity cost and comfort level as optimization targets, a home energy optimization control model for the coordinated management of hybrid energy sources is built. A smart scheduling mechanism based on the improved adaptive particle swarm optimization approach is proposed in order to derive the best time intervals for electric appliances, necessary power for the control of the room temperature for every time frame, and power for charging and discharging of the storage battery at various moments. Simulation results show that through the incorporation of distributed photovoltaic power generation, backup storage by battery, and home energy optimization control, the system efficiently balances between user comfort and electricity consumption. This offers great technical support to the development of home energy management systems. By using time-of-use electricity price for energy acquisition and supply, the optimization control goal is minimizing both power use and cost as well as preserving comfort levels. The hybrid energy management's proposed home energy optimization control model uses an adaptive particle swarm optimization algorithm to find the optimal operation schedules of the electrical appliances, the required power for temperature control in a room, and the charge/discharge power level of the storage battery at each time interval. As per the optimization principle, the proposed dynamic programming algorithm converts the multi-stage problem into a sequence of single-stage problems and solves them separately. This method successfully resolves intricate problems that cannot be addressed through greedy algorithms or divide-and-conquer. In this research, management ac

关键词： Decision making

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PFedLAH: Personalized Federated Learning with Lookahead for Adaptive Cross-modal Hashing

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Chen, Yunfei Lin, Hongyu Yang, Zhan Long, Jun Central South University Big Data Institute School of Computer Science and Engineering ChangSha410000 China Central South University Dundee International Institute of Central South University Changsha410000 China

Cross-modal hashing enables efficient cross-modal retrieval by compressing multi-modal data into compact binary codes, but traditional methods primarily rely on centralized training, which is limited when handling large-scale distributed datasets. Federated learning presents a scalable alternative, yet existing federated frameworks for cross-modal hashing face challenges like data heterogeneity and imbalance, such as non-IID data distribution across clients. To address these challenges, we propose Personalized Federated learning with Lookahead for Adaptive cross-modal Hashing (PFedLAH) method, which combines Feature Adaptive Personalized Learning (FAPL) and Weight-aware Lookahead Adaptive Selection (WLAS) mechanism together. Initially, the FAPL module is designed for the client, enabling personalized learning to mitigate the effect of divergence between server and client resulting from non-IID data distribution, while the local optimization constraint mechanism is also integrated to avoid local optimization shift and ensure better alignment with global convergence. On the server side, WLAS module combines weight-aware adaptive client selection and gradient momentum lookahead to form a dynamic and intelligent client selection scheme, while enhancing the overall convergence and consistency through lookahead gradient prediction. Comprehensive experiments on widely used datasets, including MIRFlickr-25K, MS COCO, and NUS-WIDE, comparing state-of-the-art federated hashing methods, demonstrate the superior retrieval performance, robustness, and scalability of the PFedLAH method. © 2025 IEEE. All rights reserved.

关键词： Federated learning

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RADAR: Robust Anomaly Detection and Recognition for IoT Botnet Attacks Using Hyper-Dimensional Models

RADAR: Robust Anomaly Detection and Recognition for IoT Botn...

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International Conference on Signal Processing and Communication (ICSC)

作者： Abdullah M Alqarni Faiz S Alshehri Majed S Alharbi Nitin Talreja Sajal Bhatia School of Computer Science and Engineering Sacred Heart University Fairfield USA Principal Data Engineer Optum Services Inc. (United Health Group) Hartford USA

ISBN: (数字)9798331511890

ISBN: (纸本)9798331511906

As our world continues to embrace digital technology, cyber threats have become increasingly prevalent, especially botnet attacks. Ensuring cybersecurity is a significant concern, and to address this, we need Intrusion Detection Systems (IDS) that can automatically adapt to new threats using advanced machine learning (ML) and deep learning (DL) techniques. Our project aims to develop an advanced anomaly detection and recognition system for IoT botnet attacks using hyper-dimensional models namely Random Forest, Decision Tree, Gradient Boosting, Multi-layer Perceptron (MLP), and K-Nearest Neighbor (KNN) and makes uses of hyperparameter tuning for performance optimization. By integrating an ensemble voting classifier that combines these five algorithms using a soft-voting mechanism, our results indicate an improved detection accuracy and adaptability. The proposed ensemble-based model is tested on a publicly available dataset which offers comprehensive coverage of botnet attack patterns and outperforms all individual classifiers, achieving an accuracy of 99.88% and an ROC AUC score of 0.999. The proposed approach highlights the advantages of combining hyper-dimensional classification models in effectively detecting IoT botnet attacks.

关键词： Adaptation models Accuracy Botnet Intrusion detection Signal processing algorithms Nearest neighbor methods Radar signal processing Anomaly detection Tuning Random forests

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Cloud-Enhanced Transfer Learning Framework for Medical Image Analysis: Advancing Diagnostic Speed and Accuracy in Healthcare 3

Cloud-Enhanced Transfer Learning Framework for Medical Image...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Prabu, S. Chandra, N. Subhash Bhavani, M. Varma, V. K. Chaitanya Soni, Mukesh Mahendra Institute of Technology Department of Electronics and Communication Engineering Namakkal India Cvr College of Engineering Department of Computer Science and Engineering Mangalpalle India Rajalakshmi Institute of Technology Department of Artificial Intelligence and Data Science Chennai India Sagi Rama Krishnam Raju Engineering College Department of Mechanical Engineering Bhimavaram India Dr. D. Y. Patil Vidyapeeth Dr. D. Y. Patil School of Science & Technology Pune India

ISBN: (纸本)9798331508456

When it comes to medical image analysis, problems arise due to the scarce amount of data and computational resources in medical environments. This is because, as earlier stated, cloud settings demand efficient models capable of delivering more reliable diagnostic results without straining the cloud's computing resources. In this work, we presented a transfer learning based on DenseNet-201 as well ResNet-152 architectures with a new large dataset containing 15,000 medical images of different modality. By utilizing the distributed cloud computing and hypermeters precision strategy, the proposed model was found to have 95.8 % of accuracy, 94.2% sensitivity, and 96.3%specificity. It also reduced the processing time by sixty-eight percent over standard methods across the facilities. Five different datasets validated the proposed approach, achieving an external accuracy of 92.4%, indicating improved generalization performance. The enhancement of accuracy to approximately 90% and the reduction in required computation demonstrate its feasibility for extensive clinical applications on the cloud, spanning across hospitals. © 2025 IEEE.

关键词： Distributed cloud

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Towards Fair Federated Learning via Unbiased Feature Aggregation

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： He, Zeqing Wang, Zhibo Dong, Xiaowei Sun, Peng Ren, Ju Ren, Kui Zhejiang University State Key Laboratory of Blockchain and Data Security China Institute of Blockchain and Data Security Hangzhou310027 China Wuhan University School of Cyber Science and Engineering Wuhan430072 China Hunan University College of Computer Science and Electronic Engineering Changsha410082 China Tsinghua University Department of Computer Science and Technology Beijing100084 China Zhongguancun Laboratory Beijing100094 China

Federated learning (FL) is a distributed machine learning framework that enables multiple clients to collaboratively train models without raw data exchange. Prior studies on FL mainly focus on optimizing learning performance, enhancing privacy preservation, and improving attack resilience. However, little work studies how to mitigate the unfairness of federated trained models while unfair models would make discriminatory decisions toward certain groups or populations (e.g., favoring males over females), leading to serious ethical concerns. Thus, it is crucial to mitigate model unfairness in FL, yet challenging as this requires centralized access to each data point's fairness-sensitive information (e.g., race, gender), which is prohibited in FL. In this work, we propose a novel fair FL framework FedUFA, where the server can aggregate clients' learned knowledge in an unbiased manner, to obtain fair and high-usability federated trained models. Specifically, to unearth the bias in clients' local data and account for potentially heterogeneous local models, we propose a knowledge distillation-based FL scheme, where clients' knowledge of learned features on a public dataset is amalgamated to the server for aggregation. We train an unbiased feature mapper at the server to remove fairness-sensitive latent features and extract fair representations from clients' submitted raw features. In particular, we design an adversarial training method to train the mapper, which involves a predictor aiming to maximize the prediction accuracy on the FL task and a discriminator intending to help identify fairness-sensitive features. Extensive experiments on real-world datasets demonstrate the effectiveness of FedUFA. © 2025 IEEE. All rights reserved,

关键词： Federated learning

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FDSR-INT: A Flexible On-Demand In-Band Telemetry Approach for Aerial Computing Networks

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IEEE Internet of Things Journal 2025年

作者： Xu, Jinghan Xu, Xiaolong Zhao, Juan Gao, Honghao Nanjing University of Posts and Telecommunications School of Computer Science Nanjing210023 China Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory of Big Data Security and Intelligent Processing The School of Computer Science Nanjing210023 China Jinling Institute of Technology School of Computer Science Nanjing211169 China Shanghai University School of Computer Engineering and Science Shanghai200444 China Gachon University College of Future Industry Gyeonggi-Do 461-701 Korea Republic of

In-band network telemetry (INT) is a new network measurement technique that provides real-time, fine-grained packet-level network measurements. However, standard INT lacks the flexibility to perform configurable on-demand network measurements. In this work, we propose a flexible on-demand network measurement mechanism (FDSR-INT) based on a dual bitmap by combining the programmability of segment routing based on IPv6 (SRv6) and the telemetry efficiency of the INT to achieve customizable network measurements. By designing the dual bitmap, the telemetry information of personalized probe nodes is supported, and the telemetry efficiency is improved. SRv6 is employed to direct measurement probe packets, ensuring coverage of a specified set of network targets. Its inherent programmability enables INT to conduct customized per-hop network measurements. The designed flexible INT field structure, which appends traceability information along with telemetry information, reduces the information traceability overhead in the control plane and improves the network compatibility of the mechanism. We solve the optimal probing path by solving a traveler problem in an auxiliary graph and design a greedy pathcutting algorithm to maximize the number of nodes for single packet probing while satisfying the packet length constraints to improve the success rate of the probing task. Finally, we implemented FDSR-INT using P4 and verified its performance experimentally in the constructed *** integrated simulation dynamic environment. FDSR-INT saves 30% of the data plane bandwidth and 54% of the data plane bandwidth on *** interfaces compared with SONM-SR-INT, etc. Furthermore, it has low control plane processing overhead and probe path transmission overhead. © 2014 IEEE.

关键词： Bandwidth

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Efficient Modeling Attack on Multiplexer PUFs via Kronecker Matrix Multiplication

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年

作者： Wang, Hongfei Wan, Caixue Jin, Hai Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan430074 China Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Wuhan430074 China

The Physical Unclonable Function (PUF) is valued for its lightweight nature and unique functionality, making it a common choice for securing hardware products requiring authentication and key generation mechanisms. In response to the susceptibility of individual PUFs to modeling attacks, advanced PUF variants have been developed to improve security measures. One notable type in this regard is the multiplexer-based composition of arbiter PUFs, known as MPUF, which aims to meet high reliability and security standards simultaneously. Current research on attacking MPUF encounters challenges such as substantial demands for training CRPs and low success rates. In this work, we propose a novel numerical modeling attack strategy for MPUFs. Using Kronecker products from mathematical perspective, this method precisely describe the MPUF model without using complex network architectures, boosting attack accuracy and overall efficiency. Experiment comparison with state-of-the-art works demonstrates that our method achieves better performance in terms of attack accuracy, robustness, and efficiency. Our method is able to successfully attack a (512, 8)-MPUF in 32.71 minutes with 97.14% accuracy, outperforming all existing attack methods on MPUFs. More, we validate our method through experiments with hardware implementations on FPGAs. The advantages of our method also include the adaptability to be employed to attack other MPUF variations like cMPUF and rMPUF, and the capability to be integrated with an existing attack method for launching efficient attack on MPUFs leveraging reliability information. © 2025 IEEE.

关键词： modeling attack multiplexer-based PUF Physical unclonable function

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A Novel and Efficient Multi-scale Spatio-Temporal Residual Network for Multi-class Intrusion Detection 6th

A Novel and Efficient Multi-scale Spatio-Temporal Residual ...

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6th International Conference on Machine Learning for Cyber Security, ML4CS 2024

作者： Li, Nan Gao, Zhaojian Ye, Jiabin Tang, Wei Che, Xun Chen, Yadang Nanjing Big Data Group Co. Ltd. Nanjing210003 China School of Computer Science and Engineering Southeast University Nanjing210096 China Nanjing Zhixun Interconnection Technology Co. Nanjing211100 China Hangzhou SecLead Information Technology Co. Ltd. Hangzhou310056 China Jiangsu Ruizhihe Information Technology Co. Wuxi214434 China School of Computer Science and Cyberspace Security Nanjing University of Information Science and Technology Nanjing210044 China

ISBN: (纸本)9789819645657

With the development of network devices, the network traffic presents high-dimensional, enormous as well as complex characteristics, and the network threats and attacks continue to intensify. Existing network intrusion detection models tend to disregard the extraction and learning of the temporal features of data, which will greatly affect the accuracy of network intrusion detection models. To address the shortcomings of the single structure and the inability to comprehensively learn features, we propose a novel Transformer-based network intrusion detection method, which integrates the degree of importance of traffic features and the fact that intrusion detection data has temporal and spatial characteristics. Specifically, firstly, we adopt the Transformer to perform feature extraction and construct global correlations on the input data, after that, we utilize the improved Inception to extract multi-scale features and weight the spatial features at different scales using self-attention module, in addition to that, BiGRU is employed to enhance the temporal features. Finally, the proposed model is validated on the publicly available CIC-IDS-2017 and CIC-DDoS-2019 datasets, which verifies that the proposed method outperforms the existing state-of-the-art models in terms of performance by four evaluation metrics, and also shows significant performance improvement in binary as well as multi-class classification tasks compared to other state-of-the-art methods, which proves the efficiency and effectiveness of our method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Network intrusion

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