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Federated learning-based lightweight selective feature fusion and irregular-aware network for crack detection

Optoelectronics Letters 2025年

作者： LIU Hui HE Tian CHENG Xu SHI Fan NIKOLI? Sa?a V. ZHANG Jianhua School of Computer Science and Engineering， Tianjin University of Technology Department of Electronics， Faculty of Electronic Engineering， University of Nis

Employing deep learning for pavement crack detection can significantly enhance accuracy， and federated learning can help to overcome the challenges of data silos and data security. This paper proposes a lightweight method to detect cracks. We use lightweight encoder modules to extract multi-scale features， and further feature fusion and modelling by Selective Fusion Blocks and Irregular-aware Blocks. Moreover， this method is the first to combine federated learning with crack detection， effectively resolving the tension between data privacy and data sharing in decentralised devices. Experiments were conducted to compare our proposed method with other crack detection methods on two publicly available datasets， including the original method without federated learning and five state-of-the-art methods combined with the same federated learning framework. The experiment proves that our model obtains comparable F1 score with minimal parameters and computational effort. For example， compared to the original method， the number of parameters and computation are reduced by 67.1% and 73.6%， respectively.

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Adaptive Weight Based Harris Hawks Optimization for Energy-Efficient Cluster Head Selection and Routing in Wireless Sensor Networks

Adaptive Weight Based Harris Hawks Optimization for Energy-E...

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International Conference on 6G Communications Networking and Signal Processing, SGCNSP 2023

作者： Narasimhulu, N. Issa, Ali Ashoor Mohan, A. V. Krishna Maithri, C. Kumari, C. Sapna Ananthapuramu Gooty India Dhi Qar National University of Science and Technology Nasiriyah Iraq Department of CSE Siddaganga Institute of Technology Tumakuru India Department of Computer Science and Engineering Kalpataru Institute of Technology Tiptur India School of Electronics and Communication Engineering Sapthagiri NPS University Bengaluru India

ISBN: (纸本)9789819778751

Wireless Sensor Network (WSN) consists of number of miniature Sensor Nodes (SN) utilized in number of investigating applications to sense environmental conditions. WSN collects and organizes the sensed data and the transmits to the Base Station (BS) or sink through the Sensor Nodes (SN). However, the energy carried by the sensor nodes is limited. Energy efficiency is one of the critical challenges in wireless sensor networks (WSNs). In this research, the Adaptive Weight-based Harris Hawks Optimization (AWHHO) is proposed for obtaining an energy efficient Cluster Head (CH) selection and routing in WSN. The Sensor Nodes (SN) with a predetermined less energy level qualify for the procedure of selection of CH. The optimization-based clustering and routing approach is developed for designing an optimal transmission path by CH to destination. The proposed method achieves better results and it is evaluated by various performance metrices like FND, HND LND and no. of alive nodes of values about 6750, 7980, 8300 and 50 respectively when compared to the existing methods like Energy-Efficient Clustering Algorithm (EECA), Particle Swarm Optimization (PSO) and Energy-Efficient CH by Improved Grey Wolf Optimization (EECHIGWO) respectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Particle swarm optimization (PSO)

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IoT-Assisted AI Framework for Preserving Biodiversity: Monitoring Rare Trees in Dynamic Forest Ecosystems

IoT-Assisted AI Framework for Preserving Biodiversity: Monit...

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

作者： Rajan, K. Palanivel Raj, J. Relin Francis Krishnan, R. Santhana Balamuralikrishnan, G. Vidyasree, G. Jothi, J. Nirmal Psna College of Engineering and Technology Department of Electrical and Electronics Engineering Dindigul India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India Sri Sai Ranganathan Engineering College Department of Computer Science and Engineering Coimbatore India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Avadi India

ISBN: (纸本)9798331509828

The preservation of biodiversity, particularly the monitoring of rare tree species in dynamic forest ecosystems, presents significant challenges due to the complexities in detecting and analyzing forest changes. This research introduces a novel IoT -assisted AI framework, termed FoLiA-Net (Forest Lightweight Adaptive Network), designed to improve the efficiency and accuracy of monitoring rare trees. The proposed algorithm combines IoT sensor data with machine learning techniques to adaptively detect, classify, and track rare tree species across large and dynamically changing forest areas. The framework utilizes a lightweight neural network structure to ensure real-time processing while minimizing computational resources, making it suitable for deployment in resource-constrained environments. Simulation experiments were conducted to evaluate the performance of FoLiA-Net in comparison with existing algorithms, including Faster R-CNN (F-R-CNN), Random Forest Classifier (RFC), and Gradient Boosting Machines (GBM). The evaluation was based on several key performance metrics such as accuracy, latency and detection efficiency. The results revealed that FoLiA-Net outperformed the existing algorithms in terms of accuracy and computational efficiency, demonstrating a notable improvement in the monitoring of rare tree species in diverse forest ecosystems. The proposed framework is particularly effective in large-scale forest monitoring, providing a valuable tool for biodiversity conservation efforts © 2025 IEEE.

关键词： Biodiversity Forest Ecosystems Machine Learning Rare Trees Simulation Sustainability

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On Finite Word Length Computing Error of Fixed-Point SAR Imaging Processing*

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Chinese Journal of electronics 2025年第3期23卷 645-648页

作者： Yizhuang Xie Bocheng Zhu Wen Yan Weijie Zhang School of Information and Electronics Beijing Institute of Technology Beijing China School of Electronics Engineering and Computer Science Peking University Beijing China Science and Technology on Electronic Information Control Laboratory Chengdu China

Synthetic aperture radar (SAR) imaging system is generally realized by fixed-point with the purpose of reducing system implementation scale and enhancing real-time performance. Finite word length computing error of fixed-point SAR imaging system is studied. The characteristic of computing error in SAR imaging system is analyzed. A finite word length computing error model of SAR imaging system is built, by means of which the empirical formula of system's output noise-to-signal ratio is derived. Based on the empirical formula, SAR imaging system processing word length is presented according to different processing granularities. The validity of this proposed finite word length computing error model of SAR imaging system is verified by system level fixed-point simulation.

关键词： Computational modeling Imaging Transforms Radar polarimetry Real-time systems Synthetic aperture radar System implementation

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Ensemble Artificial Neural Network and Support Vector Machine Based Parameter Evaluation in Wireless Sensor Networks

Ensemble Artificial Neural Network and Support Vector Machin...

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International Conference on 6G Communications Networking and Signal Processing, SGCNSP 2023

作者： Alkhafaji, Mohammed Ayad Yamsani, Nagendar Rashmi, P. Babu, N.V. National University of Science and Technology Dhi Qar Iraq School of Computer Science and Artificial Intelligence SR University Warangal India Inventuriz Labs Pvt. Ltd. Bengaluru India Department of Electronics and Communication Engineering SJB Institute of Technology Bengaluru India

ISBN: (纸本)9789819778751

Wireless Sensor Networks (WSNs) consists of number of wireless sensor nodes located around the geographical locations. In the deployment of WSN, there is required to know the senor deployment details in prior. The large number of data captured through the Sensor Node (SN) utilizes the SN’s minimum energy as well as confuse the data analysis at Base Station or sink for the decision making. The essential accurate evaluation of the parameters such as distance among the nodes, network lifetime, utilization of number of nodes as well as communication channel parameters. In this research, the Machine Learning (ML) algorithms of ensemble Artificial Neural Network (ANN) and Support Vector Machine (SVM) is proposed for speed up the parameters evaluations with efficient accurate results. The performance of the proposed ANN-SVM method attains better results and it achieves the network lifetime of 8345, prediction error of 0.09, Mean Square Error (MSE) of 0.005637, Mean Absolute Error (MAE) of 0.064571 and Root Mean Square Error (RMSE) of 0.07356 when compared to the existing methods such as Support Vector Regression (SVR) and Deep Neural Network. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Mean square error

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Design of Approximate Multiplier using Highly Compressed 5_2 Counter 6

Design of Approximate Multiplier using Highly Compressed 5_2...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Nelmin, Bathlin N. Shaji, R.S. Hariprasad, S. Shettygari, Sudeshna Vasan, J.K. Vijayan, V. Department of Electronics and Communication Engineering University College of Engineering Nagercoil India Department of Computer Science and Engineering St. Xavier’s Catholic College of Engineering Nagercoil India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India Department of Electronics and Communication Engineering SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (纸本)9798331522667

Approximate computing effectively impacts low-power VLSI circuits by reducing circuit overhead. It introduces minimal error in the output, which does not significantly affect the application. This paper proposes an approximate multiplier constructed using 5_3 and 5_2 counters. The proposed 5_2 counter delivers two outputs instead of three, thereby achieving 60% compression. The overhead of the 5_2 counter circuit is reduced, achieving a power delay product (PDP) improvement of 18%. This reduction comes with an addition of an 6% error rate. The newly designed multiplier has less delay, where the Mean error distance (MED) is kept as low as possible. The complexity of the 5_2 counter is also reduced since the counter uses only a full adder and three gates. This is achieved by applying various error optimization mechanisms. The error parameters, (i) Error Distance (ED), and (ii) Mean Error Distance (MED) are analyzed and compared with existing works. The proposed multipliers are involved in image contrast application with better Peak Signal to Noise Ratio (PSNR) value. © 2025 IEEE.

关键词： Delay circuits

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Application of hybrid capsule network model for malaria parasite detection on microscopic blood smear images

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Multimedia Tools and Applications 2025年第7期84卷 3459-3485页

作者： Kumar, S. Aanjan Muchahari, Monoj Kumar Poonkuntran, S. Kumar, L. Sathish Dhanaraj, Rajesh Kumar Karthikeyan, P. Department of Computer Application and Science Institute of Engineering and Management West Bengal Kolkata India School of Computing Science and Engineering VIT Bhopal University Bhopal-Indore Highway Kothrikalan Madhya Pradesh Sehore466114 India Pune India Department of Electronics & Communication Engineering Velammal College of Engineering & Technology Tamil Nadu Madurai625009 India

Today, malaria is a dangerous disease caused by Plasmodium parasites and transmitted by the bite of Anopheles mosquitoes. It is estimated that more than 200,000 cases of malaria will occur every day worldwide, and more than 400,000 people will die from malaria. Early diagnosis of malaria is important to mitigate the morbidity and mortality of the disease. In this paper, a fully trained capsule neural network combined with a convolutional neural network model is proposed for malaria detection from blood smear microscopic images. In malaria diagnosis, the hybrid CapsNet model can detect malaria in blood samples. The hybrid model is optimized based on malaria data in microscopic images of infected and uninfected blood. Images are processed and enhanced through rotation before being fed into the hybrid CapsNet model. The CapsNet hybrid model is examined for malaria data with a learning rate of 0.07 and a batch size of 20. Hybrid CapsNet model shows improved results in detecting malaria from microscopic images measured against the currently available standard malaria test. Constraints like malaria parasite detection rate, accuracy, F1 index and blood smear classification is used to gauge the attainment of the hybrid CapsNet model. Detection rate of 99%, accuracy of 99.08% and FAR of 0.97% is attained by the hybrid CapsNet model as contrary to the traditional state-of-the-art model, the DSCN-Net, where the detection rate was 98%, the accuracy was 97.2%, and the FAR was 0.99%. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Diseases

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Unveiling House Price with Machine Learning Algorithm

Unveiling House Price with Machine Learning Algorithm

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

作者： Kusuma, Rambabu Kumar, Kalagadda Kiran Pillutla, Siva Ramakrishna Saikumar, Aruru Vignan's Foundation For Science Technology and Research Department of Computer Science and Engineering Andhra pradesh Guntur India School of Electronics Engineering VIT-AP University Amaravati522241 India Vnr Vjiet Dept of Ece Bachupally India

ISBN: (纸本)9798331509828

A frequently overlooked and yet very critical factor in having a high quality of life is the perfecting of one's living environment. A house should be quiet, unique, and instill peace in the soul of its residents, attaining individual needs across many aspects. Therefore, finding such a house with great architecture and making the right choice is essential. Beyond location, the structure and cost of a house are key elements that significantly influence the suitability of a place to live. Accurate price predictions are crucial to address potential future concerns. This study presents a model designed to help individuals select an ideal home based on their financial circumstances. It evaluates various machine learning models, including the Random Forest Regressor and Gradient Boosting Regressor, to ensure reliable predictions. The proposed model aims to present estimates as closely as possible aligned with ideal conditions, considering individual preferences and demands. © 2025 IEEE.

关键词： Adversarial machine learning

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A Survey on Digital Twins: Enabling Technologies, Use Cases, Application, Open Issues, and More

IEEE Journal of Selected Areas in Sensors

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IEEE Journal of Selected Areas in Sensors 2025年 2卷 84-107页

作者： Hassija, Vikas Chamola, Vinay De, Rajdipta Das, Soham Chakrabarti, Arjab Sangwan, Kuldip Singh Pandey, Amit Deemed to be University School of Computer Engineering Bhubaneswar751024 India BITS-Pilani Department of Electrical and Electronics Engineering Pilani Campus Pilani333031 India BITS Pilani Department of Mechanical Engineering Pilani Campus Pilani333031 India Bennett University Computer Science Engineering and Technology Department Greater Noida201310 India

Digital Twins, sophisticated digital replicas of physical entities, have been gaining significant attention, especially after NASA's endorsement, and are poised to revolutionize numerous fields, such as medicine and construction. These advanced models offer dynamic, real-time simulations, leveraging enabling technologies, such as artificial intelligence, machine learning, IoT, cloud computing, and Big Data analytics to enhance their functionality and applicability. In the medical field, Digital Twins facilitate personalized treatment plans and predictive maintenance of medical equipment by simulating human organs with precision. In construction, they enable efficient building design and urban planning, optimizing resource use, and reducing costs through predictive maintenance. Startups are innovatively employing Digital Twins in various sectors, from smart cities - where they optimize traffic flow, energy consumption, and waste management - to industrial machinery, ensuring predictive maintenance and minimizing downtime. This survey delves into the diverse use cases, market potential, and challenges of Digital Twins, such as data security and interoperability, while emphasizing their transformative impact on industries. The future prospects are promising, with continuous advancements in AI, ML, IoT, and cloud computing driving further expansion and application of Digital Twin technologies. © 2024 IEEE.

关键词： Predictive maintenance

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RIS-Assisted Flexible Receive Spatial Modulation for Wireless Communication

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IEEE Wireless Communications Letters 2025年

作者： Yue, Ming Peng, Yuyang Jin, Qi Lee, Juho Macau University of Science and Technology School of Computer Science and Engineering China Samsung Electronics Samsung Research Seoul Korea Republic of

In this letter, we propose a novel transmission scheme called Reconfigurable Intelligent Surface (RIS)-assisted Flexible Receive Spatial Modulation (RIS-FRSM). In this scheme, the number of active Receive Antennas (RAs) is dynamically determined by additional information bits, and the RIS is divided into corresponding parts to facilitate the flexible configuration. It not only achieves high Spectral Efficiency (SE) transmission but also allows for the adjustment of system settings according to diverse communication requirements. Furthermore, we derive an upper bound for the average Bit Error Rate (BER). The results of computer simulations demonstrate that the proposed scheme outperforms the benchmark schemes in terms of error performance. © 2012 IEEE.

关键词： Signal modulation

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