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Toward Collaborative Intelligence for Meta-Computing-Driven IIoT Based on Vertical Federated Learning With Fast Convergence

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IEEE Internet of Things Journal 2025年第10期12卷 13806-13816页

作者： Li, Youqi Liu, Shuangji Meng, Yanchen Qi, Shenyi Qu, Zhe Li, Fan Wang, Yu Beijing Institute of Technology School of Computer Science and Technology Beijing100081 China Central South University School of Computer Science and Engineering Changsha410083 China Temple University Department of Computer and Information Sciences PhiladelphiaPA19122 United States

Industrial Internet of Things (IIoT) is an emerging technology that digitizes industrial production and realizes Industry 4.0. However, it shows that IIoT is difficult to enable sophisticated downstream applications without eliciting all devices to achieve collaborative intelligence. Existing works on IIoT either require the consolidation of various IIoT devices’ data into a single centralized server which has potential privacy breach, or coordinate devices to learn a global model in privacy-preserving federated learning (FL) but assume data across devices has the sample feature space and neglect the heterogeneity of IIoT devices. In this article, we propose Meta-computing-driven vertical FL (VFL) algorithms to achieve collaborative intelligence in IIoT where heterogeneous devices have imperfect data with incomplete features. Specifically, we first provide the modeling of N devices’ VFL to collectively train the submodels and the common model. We present the computing graph to clearly indicate the gradient evaluation. To enable a fast convergence performance, we design a variance-reduced gradient estimator that can be seamlessly integrated into the basic VFL. Finally, we evaluate our proposed VFL by conducting experiments on the MNIST dataset regarding image recognition and the DAWM dataset for detecting anomalies in wafer manufacturing. The experimental results show that our VFL for IIoT is both effective and efficient. © 2014 IEEE.

关键词： Federated learning

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Optimized Deep Learning Framework for Cryptocurrency Price Prediction

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SN computer Science 2025年第1期6卷 1-16页

作者： Shirwaikar, Rudresh Naik, Sagar Pardeshi, Abiya Manjrekar, Sailee Shetye, Yash Dhargalkar, Siddhesh Madkaikar, Ritvik Agnel Institute of Technology and Design Computer Engineering Goa Mapusa India

Cryptocurrency has played a major role in the growth of digital currency exchange in the current financial environment. The study estimates the values of three cryptocurrencies—Litecoin, Ethereum, and Monero—using deep learning techniques. “Long Short-Term Memory” (LSTM) and “Gated Recurrent Unit” (GRU). Long-term dependencies in sequential data are ideal for recurrent neural networks like LSTM and GRU. The study also used the Direction Algorithm (DA), which takes Bitcoin as a parent coin, to determine its direction, which influences the price of other cryptocurrencies, and Change Point Detection (CPD) approaches, which use Pruned Exact Linear Time (PELT) to detect rapid price variations. In addition, this study uses the Vader algorithm for news sentiment analysis to increase forecast accuracy. Combining Vader, the direction algorithm, and PELT gives the study more depth. The standard LSTM and GRU, as well as the proposed optimized (Vader + direction + PELT) LSTM and GRU, are tested on all three cryptocurrencies for prediction. The proposed LSTM predicted the price of Monero, Ethereum, and Litecoin with an error rate of MAE 1.53, 18.84, and 1.66, respectively. Whereas the proposed GRU model achieved MAE values of 1.11, 16.10, and 1.57 for Monero, Ethereum, and Litecoin price predictions respectively. The obtained results indicate that both the proposed optimized model of LSTM and GRU demonstrated higher accuracy in price prediction compared to standard LSTM and standard GRU models. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Change point detection Cryptocurrency Deep learning Gated recurrent unit Long short-term memory

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An Improved Fuzzy Grounded Dynamic Package Priority Resolve and Line Management System in Wireless Sensor Network

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SN computer Science 2025年第2期6卷 1-12页

作者： Songare, Lokendra Singh Kumhar, Bhupchand Rajput, Ajeet Singh Shukla, Prashant Kumar Nigam, Ankita Gautam, Prakhar Ahluwalia, Ritu Department of Computer Science and Engineering Medicaps University MP Indore India Department of Computer Science & Engineering IES Institute of Technology and Management IES University MP Bhopal India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation K L Deamed to be University Vaddeswaram Andhra Pradesh Guntur 522302 India Princeton Institute of Engineering and Technology for Women Telangana Hyderabad India Department of Computer Science and Engineering Gyan Ganga Institute of Technology and Sciences MP Jabalpur India Department of CSBS Gyan Ganga Institute of Technology and Sciences MP Jabalpur India

In Wireless Sensor Networks, it is crucial to schedule packets efficiently while taking priorities into account. It helps congestion avoidance algorithms decide on rate adjustments and packet discarding and mitigates the performance hit from WSN’s resource constraints. The current PPI method works well for use cases where the threshold values are already known. FDMP (Fuzzy Based Dynamic Packet Priority Determination and Line Management) is presented in this article. FDMP is a congestion-aware packet scheduling algorithm that offers robust and universal mechanisms for dynamically determining packet priority and efficient line management. Therefore, it achieves minimum end-to-end delay for high priority packets while providing adequate fairness for low priority packets by scheduling packets within each line according to their execution time. From our simulations, we can deduce that the FDMP decreases end-to-end transmission delay and jitter while increasing packet delivery ratio, average residual energy, and throughput. Congestion in the network can be avoided through the optimal scheduling of packets to prevent packet retransmission and the removal of packets with expired deadlines from the transmission medium. When compared to the Dynamic Multilevel Priority (DMP) line scheduling approach, which is also a resource control algorithm, FDMP demonstrated superior performance. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： DMP FDMP PPI QOS WSN

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Industry 5.0: a conceptual cybersecurity model for secured digital transformation of enterprises

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EDPACS 2025年第4期70卷 1-38页

作者： Chaudhuri, Abhik R, Jayalakshmi Kumar Bala, Pradip Shoemaker, Daniel Kumar Behera, Rajat School of Computer Engineering Kalinga Institute of Industrial Technology (KIIT) India

The diverse range of emerging technologies from Industry 4.0 presented enterprises with significant opportunities to develop new product and service models. However, increased cybersecurity threats and attacks in Industry 4.0 pose a significant challenge to the operations of enterprises globally. The rapid pace of digital transformation of enterprises, combined with inadequate security controls, is creating vulnerabilities for potential cyber-attacks. To address Industry 4.0‘s constraints, enterprises are contemplating the Industry 5.0 paradigm. The cybersecurity challenges of Industry 4.0 should be addressed in Industry 5.0 to increase operational resilience and trustworthiness. To address these challenges in Industry 5.0 the key cybersecurity focus areas for enterprises have been identified in this article considering the evolving threats. Utilizing design science research methodology and design knowledge framework, a conceptual cybersecurity model has been recommended in this article on the security aspects of technology, process, people, and services to assist enterprises of all sizes in Industry 5.0 to proactively address the cybersecurity threats in their digital transformation journey. © Copyright 2025 Taylor & Francis–All rights reserved.

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Genetic Algorithm-optimized k-nearest Neighbors and Support Vector Machines for Breast Cancer Detection in Resource-constrained Environments

IAENG International Journal of Computer Science

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IAENG International Journal of computer Science 2025年第3期52卷 848-861页

作者： Alemu, Abebe Girma, Anteneh Abebe, Mesfin Srinivasagan, Ramasamy Adama Science and Technology University Adama Ethiopia Computer Science/Cyber-Security department University of the District of Columbia WashingtonDC United States Computer Science and Engineering department Adama Science and Technology University Adama Ethiopia Computer Engineering department CCSIT King Faisal University Al Hufuf Saudi Arabia

Breast cancer poses a significant global threat, highlighting the urgent need for early detection to reduce mortality rates. Researchers are working to minimize the occurrence of false positives and false negatives, thereby improving the efficiency of breast cancer detection models. To achieve this, they employ advanced techniques such as artificial intelligence, machine learning, deep learning, and computational intelligence. Support vector machines (SVM) and k-nearest neighbors (KNN) are two popular lightweight machine-learning techniques.;however, their effectiveness depends on proper feature selection and parameter tuning. Genetic algorithm optimization provides a solution by intelligently selecting relevant features and fine-tuning parameters, which enhances classification accuracy for early diagnosis. This study demonstrates the effectiveness of a hybrid computational intelligence model that utilizes genetic algorithms for feature selection. The proposed GAKNN-SVM model shows superior performance in detecting breast tumors, utilizing the Wisconsin Breast Cancer Diagnostic Dataset. The results indicate significant improvements, with accuracy, sensitivity, and specificity rates reaching 98.25%, 98.15%, and 98.41%, respectively, based on 171 test samples. Overall, genetic algorithms and machine learning approaches hold great promise for improving breast cancer detection accuracy, ultimately leading to better diagnostic outcomes and reduced mortality rates, especially in resource-constrained environments. © (2025), (International Association of Engineers). All rights reserved.

关键词： Support vector machines

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Customized Graph-Cut Segmentation with Superpixels for Snowfall Detection on Solar Panels 4

Customized Graph-Cut Segmentation with Superpixels for Snowf...

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

作者： Pavithra, V. Danisha, B.R. Divyadharshini, V. Koresh, H. James Deva Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Kpr Institute of Engineering and Technology Department of Electronics and Communication Engineering Coimbatore India

ISBN: (纸本)9798331523923

Snow accumulation on solar panels reduces their performance in energy generation regions. Hence certain image processing methods were developed for segmenting snow from the normal areas of solar panels. Graph Cut and Flood Fill are the popular semi-automatic method used for this application where it relies on detecting an efficient region for marking the segmentation process. Similarly, the manual intervention fails to provide consistent results in such methods. Therefore, a customized graph cut methodology is proposed in this work by combining super pixel-based feature extraction and k-means clustering. At first, the input images are preprocessed by dividing the pixels to form super pixels as a group to reduce the computational complexity without losing the meaningful information. Features like intensity, edge density, texture, and color are extracted from these super pixel groups for segmenting the snow-covered regions on a solar panel. Further, the extracted features are normalized by a k-means clustering algorithm for identifying the highest intensity regions to observe the snow-covered regions. A graph cut-based segmentation technique is applied at the end to ensure the precise boundary delineation. The work is also incorporated with several post-processing techniques for improving the segmentation mask quality. This proposed method is evaluated against Graph cut and flood fill and satisfied with an average dice similarity coefficient of 93.09% and 95.11%. © 2025 IEEE.

关键词： Image segmentation

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Orbit-Orbit Interaction in Spatiotemporal Optical Vortex

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engineering 2025年第2期45卷 44-51页

作者： Jian Chen Jie Zhao Xi Shen Dewei Mo Cheng-Wei Qiu Qiwen Zhan School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai 200093China Shanghai Key Laboratory of Modern Optical System University of Shanghai for Science and TechnologyShanghai 200093China Department of Electrical and Computer Engineering National University of SingaporeSingapore 117583Singapore

While spin-orbit interaction has been extensively studied,few investigations have reported on the interaction between orbital angular momenta(OAMs).In this work,we study a new type of orbit-orbit coupling between the longitudinal OAM and the transverse OAM carried by a three-dimensional(3D)spatiotemporal optical vortex(STOV)in the process of tight *** 3D STOV possesses orthogonal OAMs in the x-y,t-x,and y-t planes,and is preconditioned to overcome the spatiotemporal astigmatism effect.x,y,and t are the axes in the spatiotemporal *** corresponding focused wavepacket is calculated by employing the Debye diffraction theory,showing that a phase singularity ring is generated by the interactions among the transverse and longitudinal vortices in the highly confined *** Fourier-transform decomposition of the Debye integral is employed to analyze the mechanism of the orbit-orbit *** is the first revelation of coupling between the longitudinal OAM and the transverse OAM,paving the way for potential applications in optical trapping,laser machining,nonlinear light-matter interactions,and more.

关键词： Spatiotemporal optical vortex Orbital angular momentum Momentum interaction Highly confined wavepacket Diffraction

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Beamforming and Reflection Design for Short Packet ISAC With Non-Ideal RIS: An A3C-Based Approach

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IEEE Transactions on Vehicular technology 2025年第5期74卷 8379-8384页

作者： Mahmoudi, Behrad Khonsari, Ahmad Zeinali, Farshad Mili, Mohammad Robat Mashhadi, Mahdi Boloursaz Xiao, Pei University of Tehran Department of Electrical and Computer Engineering Tehran Iran University of Tehran School of Electrical and Computer Engineering College of Engineering Tehran Iran School of Computer Science in the Institute for Research in Fundamental Sciences Tehran Iran United Kingdom Institute for Advanced Innovative Solutions Tehran Iran

Integrated sensing and communication (ISAC) is a promising solution to mitigate the increasing congestion of the wireless spectrum. In this paper, we investigate the short packet communication regime within an ISAC system assisted by a reconfigurable intelligent surface (RIS) to meet the low latency ultra-reliable requirements in the next-generation wireless networks. We consider a non-ideal RIS model that captures effects of the phase-dependent amplitude variations in the reflection coefficients, and we have incorporated the near-field model into the channels between the RIS and the users or targets. In this setup, we jointly design the transmit beamforming and the RIS phase shifts to maximize the sum rate while satisfying the sensing signal-to-noise ratio (SNR) requirement. The system simultaneously carries out multitarget sensing and multi-user short packet communications with the help of the RIS. Considering the non-convex and dynamic nature of the resulting optimization problem, we propose an asynchronous advantage actor-critic (A3C) based method for beamforming and reflection design in this setup. Numerical results demonstrate the superiority of the proposed scheme over the benchmarks. © 1967-2012 IEEE.

关键词： Beamforming

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MediServe:An IoT-Enhanced Deep Learning Framework for Personalized Medication Management for Elderly Care

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computers, Materials & Continua 2025年第4期83卷 935-976页

作者： Smita Kapse Ganesh Yenurkar Vincent Omollo Nyangaresi Gunjan Balpande Shravani Kale Manthan Jadhav Sahil Lawankar Vikrant Jaunjale Department of Computer Technology Yeshwantrao Chavan College of EngineeringWanadongariNagpur441110MaharashtraIndia Computer Science&Engineering Jaramogi Oginga Odinga University of Science&TechnologyBondo40601Kenya Department of Applied Electronics Saveetha School of EngineeringSIMATSChennai602105TamilnaduIndia

In today’s fast-paced world,many elderly individuals struggle to adhere to their medication schedules,especially those with memory-related conditions like Alzheimer’s disease,leading to serious health risks,hospital-izations,and increased healthcare *** reminder systems often fail due to a lack of personalization and real-time *** address this critical challenge,we introduce MediServe,an advanced IoT-enabled medication management system that seamlessly integrates deep learning techniques to provide a personalized,secure,and adaptive *** features a smart medication box equipped with biometric authentication,such as fingerprint recognition,ensuring authorized access to prescribed medication while preventing misuse.A user-friendly mobile application complements the system,offering real-time notifications,adherence tracking,and emergency alerts for caregivers and healthcare *** system employs predictive deep learning models,achieving an impressive classification accuracy of 98%,to analyze user behavior,detect anomalies in medication adherence,and optimize scheduling based on an individual’s habits and health ***,MediServe enhances accessibility by employing natural language processing(NLP)models for voice-activated interactions and text-to-speech capabilities,making it especially beneficial for visually impaired users and those with cognitive ***-based data analytics and wireless connectivity facilitate remote monitoring,ensuring that caregivers receive instant alerts in case of missed doses or medication ***,machine learning-based clustering and anomaly detection refine medication reminders by adapting to users’changing health *** combining IoT,deep learning,and advanced security protocols,MediServe delivers a comprehensive,intelligent,and inclusive solution for medication *** innovative approach not only improves the quality of life for elderly

关键词： MediServe medication health risks smart medication box

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An Effective Yet Fast Early Stopping Metric for Deep Image Prior in Image Denoising

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IEEE Signal Processing Letters 2025年 32卷 1925-1929页

作者： Cheng, Xiaohui Xu, Shaoping Tao, Wuyong Nanchang University School of Mathematics and Computer Sciences Nanchang330031 China Nanchang Institute of Technology School of Computer Information Engineering Nanchang330044 China

The deep image prior (DIP) and its variants have demonstrated the ability to address image denoising in an unsupervised manner using only a noisy image as training data, but practical limitations arise due to overfitting in highly overparameterized models and the lack of robustness in the fixed iteration step of early stopping, which fails to adapt to varying noise levels and image contents, thereby affecting denoising effectiveness. In this work, we propose an effective yet fast early stopping metric (ESM) to overcome these limitations when applying DIP models to process synthetic or real noisy images. Specifically, our ESM measures the image quality of the output images generated by the DIP network. We split the output image from each iteration into two sub-images and calculate their distance as an ESM to evaluate image quality. When the ESM stops decreasing over several iterations, we end the training, ensuring near-optimal performance without needing the ground-truth image, thus reducing computational costs and making ESM suitable for application in the denoising of real noisy images. © 1994-2012 IEEE.

关键词： Image denoising

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