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Real-time risk and behavioral monitoring in IoT applications using soft computing techniques

Journal of Ambient Intelligence and Humanized Computing 2023年第11期14卷 15159-15159页

作者： Jeon, Gwanggil Anisetti, Marco Chehri, Abdellah Damiani, Ernesto Department of Embedded Systems Engineering Incheon National University Incheon Korea Republic of Università degli Studi di Milano Milan Italy Department of Mathematics and Computer Science Royal Military College of Canada Kingston Canada Department of Electrical and Computer Engineering Khalifa University Abu Dhabi United Arab Emirates

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Theoretical Bound and Compensation for Residual Error Probability of GRAND-CRC-Based Functional Safety Communication

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IEEE Transactions on Communications 2025年

作者： Zhan, Ming Pang, Zhibo Wu, Fang Zhou, Qiang Zhang, Shiqing Zhang, Jianwu Yu, Kan Taizhou University College of Electronic and Information Engineering Zhejiang Taizhou318000 China ABB Corporate Research Forskargränd 7 VästeråsSE-721 78 Sweden School of Electrical Engineering and Computer Science Department of Intelligent Systems StockholmSE-114 28 Sweden Southwest University College of Economics and Management Chongqing400715 China Hangzhou Dianzi University College of Telecommunication Engineering Hangzhou310018 China La Trobe University Bendigo Campus School of Engineering and Mathematical Sciences Department of Computer Science and Information Technology Edwards Rd Flora HillVIC3552 Australia

In modern Industrial Internet of Things (IIoT) ecosystems, functional safety protocols are increasingly utilized to transmit safety protocol data unit (SPDU). Integrating the universal guessing random additive noise decoding (GRAND) algorithm with cyclic redundancy check (CRC)-coded SPDU can minimize SPDU retransmissions. However, it introduces residual error probability (REP) degradation that requires careful consideration. Using the IEC 61784-3 Standard and CRC assumptions, we derive a closed-form REP evaluation formula specific to SPDU length and maximum error correction capability. Our analysis reveals that, under the worst industrial conditions and for an SPDU length of 128 bits, the REP performance degrades by approximately 2:2 × 102 times when the CRC signature length is 24 bits and up to one bit is guessing decoded. This degradation becomes more pronounced with increased error correction capability. To address this, we propose to compensate the REP degradation by adopting longer CRC signature. This paper provides a theoretical framework for adopting the GRAND algorithm in functional safety communication, setting a foundation for enhancing reliability in IIoT applications. © 1972-2012 IEEE.

关键词： Decoding

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An Optimized Framework Development of ABC Algorithm Along with SVMP Algorithm for Lung Cancer Detection

An Optimized Framework Development of ABC Algorithm Along wi...

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Advance Computing and Innovative Technologies in Engineering (ICACITE), International Conference on

作者： M. Rajasekar P Arunachalam P Priyadharsini N Lavanya Devi Huda Hayder Abbas Shams A. Al-Qaisy Institute of Department of Computer Science and Engineering Saveetha School oof Engineering Saveetha Institute of Medical and Technical Science SIMATS University Department of biomedical engineering VelTech Rangarajan Dr.Sugunthala R&D Institute of Science and Technology Chennai Department of Computer Science and Business Systems Nehru Institute of Engineering and Technology Coimbatore Tamilnadu Department of ECE PSNA College of Engineering and Technology Dindigul India Collage of Pharmacy National University of Science and Technology Dhi Qar Iraq Al-Farahidi university Baghdad Iraq

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

In medicine, early detection of lung cancer is essential for successful treatment regimens. Despite the small sample size, cancer databases often include gene expression levels as attributes. Therefore, in order to increase the convergence speed of the classification algorithm, redundant features should be eliminated. In this work, we present a unique hybrid method for cancer classification called artificial bee colony with support vector machine selection. This approach for early search of a subset of genes for cancer prognosis optimizes feature selection by combining cuckoo search algorithm (CSA) and Spider-Monkey optimization (SMO) algorithm In order with increased accuracy of artificial bee colony method, we perform Minimum Redundancy Maximum Relevance (mRMR).) method, we reduce the redundancy in gene expression data from the cancer dataset and then these selected gene subsets are efficiently classified into different cancer clusters or groups using deep learning (DL) techniques. We evaluate the effectiveness of our proposed method against eight reference microarray gene expression data sets of various cancers. Classification performance is evaluated using measures such as confusion matrix, F1-score, recall, and accuracy. Our findings show that, when applied to large gene expression datasets linked to cancer, the suggested gene selection strategy in conjunction with DL provides improved classification accuracy compared to other existing DL and machine learning methods.

关键词： Support vector machines Accuracy Machine learning algorithms Redundancy Lung cancer Clustering algorithms Vectors

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BIOMIMETIC ANCHORING SYSTEM FOR SUSTAINED AND LOCALIZED GASTROINTESTINAL DRUG DELIVERY

BIOMIMETIC ANCHORING SYSTEM FOR SUSTAINED AND LOCALIZED GAST...

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2022 Solid-State Sensors, Actuators and Microsystems Workshop, Hilton Head 2022

作者： Levy, Joshua A. Straker, Michael A. Beardslee, Luke A. Ghodssi, Reza Department of Electrical and Computer Engineering University of Maryland College ParkMD United States Institute for Systems Research University of Maryland College ParkMD United States Department of Materials Science and Engineering University of Maryland College ParkMD United States Fischell Department of Bioengineering University of Maryland College ParkMD United States Robert E. Fischell Institute for Biomedical Devices University of Maryland College ParkMD United States

ISBN: (纸本)9781940470047

We present a novel application of biomimetic spiny microneedles for an anchoring drug deposit (SMAD) enabling long-term localized treatment of lesions within the GI tract. Mechanical characterizations in ex-vivo intestinal tissue demonstrate a 22-fold higher anchoring force than traditional conical molded microneedle (MMN) arrays. Additionally, payload diffusion experiments demonstrate that the SMAD can predictably deliver drugs, locally, to a site of interest with comparable performance to MMNs, indicated by strong logarithmic correlation (R2=0.9773) across SMAD and MMN data. We anticipate this work will permit exploration of localized GI tract drug delivery, facilitating targeted treatment to reduce adverse drug side effects. © 2022 TRF.

关键词： Tissue

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Fine-Tuned Yolo V-10X: Real-Time Object Detection for Autonomous Vehicles Under Multiple Weather Conditions

Fine-Tuned Yolo V-10X: Real-Time Object Detection for Autono...

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IEEE SSD International Multi-Conference on systems, Signals and Devices

作者： Safa Teboulbi Seifeddine Messaoud Mohamed Ali Hajjaji Mohamed Atri Abdellatif Mtibaa Higher Institute of Computer Sciences and Mathematics of Monastir Monastir University Monastir Tunisia Faculty of Sciences of Monastir Monastir University Monastir Tunisia Higher Institute of Applied Sciences and Technology of Sousse Sousse University Sousse Tunisia Computer Engineering Department College of Computer Science King Khalid University Abha Saudi Arabia Systems Integration & Emerging Energies Laboratory National Engineering School of Sfax Sfax University Sfax Tunisia

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

Detecting objects such as vehicles, buildings, pedestrians, and road signs is indispensable to advancing the concept of autonomous and self-driving cars. Furthermore, an autonomous vehicle (AV) must accurately detect its surrounding environment to operate reliably. Most object detection (OD) techniques perform adequately under typical weather conditions, including cloudy or sunny days. However, their efficiency decreases significantly when exposed to Adverse Weather Conditions (AWCs), including days with sandstorm, rain, fog or snow. Complex and computationally costly models are required to achieve high accuracy rates. In this study, we present an improved OD system in AWCs for autonomous vehicles (AVs) using the single-stage deep learning (DL) algorithm YOLO (You Only Look Once) version 10. To evaluate our system, Vehicle Detection in Adverse Weather Nature (DAWN) dataset is used. It comprises real-world images captured under various types of AWCs. The experimental findings confirm that the suggested method is effective and surpasses state-of-the-art OD approaches under AWCs.

关键词： YOLO Adaptation models Accuracy Rain Smart cities Snow Roads Real-time systems Reliability Meteorology

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Realizing Emotional Interactions to Learn User Experience and Guide Energy Optimization for Mobile Architectures 22

Realizing Emotional Interactions to Learn User Experience an...

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Proceedings of the 55th Annual IEEE/ACM International Symposium on Microarchitecture

作者： Xueliang Li Zhuobin Shi Junyang Chen Yepang Liu National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China College of Computer Science and Software Engineering Shenzhen University China Research Institute of Trustworthy Autonomous Systems Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation and Department of Computer Science and Engineering Southern University of Science and Technology China

ISBN: (纸本)9781665462723

In the age of AI, mobile architectures such as smartphones are still "cold machines"; machines do not feel. If the architecture is able to feel users' feelings and runtime user experience (UX), it will accordingly adapt performance/energy to find the optimal system-operating state that consumes the least energy to satisfy users. In this paper, we will utilize users' facial expressions (FEs) to learn their runtime UX. We know that FEs are the natural and direct way for humans to convey their emotions and feelings. Our study reveals that FEs also reflect UX. Our research for the first time quantifies the link between FEs and UX. Leveraging this link, the architecture will be able to use the front camera to see FEs and feel users' UX. Based on UX, the architecture can appropriately provision computing resources. We propose Vi-energy system to realize the above idea. Our evaluation shows that Vi-energy reduces energy consumption by 52.9% at maximum and secures UX.

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Systematic Review: Predictive Models for the Winning Team of Super Leagues (SL)

Systematic Review: Predictive Models for the Winning Team of...

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Business Analytics for Technology and Security (ICBATS), International Conference on

作者： Salman Muneer Muhammad Bux Alvi Mahmoud Al Sakhnani Hamamd Raza Taher M. Ghazal Munir Ahmad School of Computer Science NCBA&E Lahore Pakistan Applied Science Research Center Applied Science Private University Amman Jordan Department of Computer Systems Engineering The Islamia University of Bahawalpur Bahawalpur Pakistan Skyline University College School of Business University City Sharjah Sharjah UAE School of Computer Science National College of Business Administration & Economics Lahore Pakistan Faculty of Information Science and Technology Center for Cyber Security Universiti Kebangsaan Malaysia (UKM) Bangi Malaysia

The Pakistan Super League (PSL) is one of the most popular cricket leagues in the world, attracting millions of fans and spectators each year. With the 2023 edition of the league just around the corner, the use of predictive models to determine the winning team has gained much attention. This systematic review analyzed the existing literature on predictive models for determining the winning team of PSL 2023. The study examined multiple approaches, including machine learning, statistical modeling, and expert opinion. The results of this review suggest that machine learning models, particularly ensemble models, are the most accurate predictors of PSL match outcomes. These models typically use a combination of features, such as team rankings, player performance statistics, match-winning ratio, and previous match outcomes, to predict the result of a match. This paper discovered that expert opinion-based models could provide valuable insights into the likely winner of a match. These models typically use input from cricket experts and analysts to predict the outcome of a match.

关键词： Ranking (statistics) Analytical models Fans Systematics Machine learning Predictive models History

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The applied principles of EEG analysis methods in neuroscience and clinical neurology

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Military Medical Research 2024年第6期11卷 907-941页

作者： Hao Zhang Qing-Qi Zhou He Chen Xiao-Qing Hu Wei-Guang Li Yang Bai Jun-Xia Han Yao Wang Zhen-Hu Liang Dan Chen Feng-Yu Cong Jia-Qing Yan Xiao-Li Li School of Systems Science Beijing Normal UniversityBeijing 100875China College of Electrical and Control Engineering North China University of TechnologyBeijing 100041China School of Automation Science and Engineering South China University of TechnologyGuangzhou 510641China Department of Psychology the State Key Laboratory of Brain and Cognitive Sciencesthe University of Hong KongHong Kong SAR 999077China HKU-Shenzhen Institute of Research and Innovation Shenzhen 518057GuangdongChina Department of Health Technology and Informatics the Hong Kong Polytechnic UniversityHong Kong SAR 999077China Department of Rehabilitation Medicine the First Afliated Hospital of Nanchang UniversityNanchang 330006China Rehabilitation Medicine Clinical Research Center of Jiangxi Province Nanchang 330006China Beijing Key Laboratory of Learning and Cognition School of PsychologyCapital Normal UniversityBeijing 100048China School of Communication Science Beijing Language and Culture UniversityBeijing 100083China Institute of Electrical Engineering Yanshan UniversityQinhuangdao 066004HebeiChina School of Computer Science Wuhan UniversityWuhan 430072China School of Biomedical Engineering Faculty of Electronic Information and Electrical EngineeringDalian University of TechnologyDalian 116081LiaoningChina Guangdong Artifcial Intelligence and Digital Economy Laboratory(Guangzhou) Guangzhou 510335China.

Electroencephalography(EEG)is a non-invasive measurement method for brain *** to its safety,high resolution,and hypersensitivity to dynamic changes in brain neural signals,EEG has aroused much interest in scientific research and medical *** article reviews the types of EEG signals,multiple EEG signal analysis methods,and the application of relevant methods in the neuroscience feld and for diagnosing neurological ***,3 types of EEG signals,including time-invariant EEG,accurate event-related EEG,and random event-related EEG,are ***,5 main directions for the methods of EEG analysis,including power spectrum analysis,time-frequency analysis,connectivity analysis,source localization methods,and machine learning methods,are described in the main section,along with diferent sub-methods and effect evaluations for solving the same ***,the application scenarios of different EEG analysis methods are emphasized,and the advantages and disadvantages of similar methods are *** article is expected to assist researchers in selecting suitable EEG analysis methods based on their research objectives,provide references for subsequent research,and summarize current issues and prospects for the future.

关键词： Electroencephalogram analysis methods Applied principles Neuroscience Diagnosis Neurological diseases

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Privacy-Aware Joint DNN Model Deployment and Partition Optimization for Delay-Efficient Collaborative Edge Inference

arXiv

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arXiv 2025年

作者： Cheng, Zhipeng Xia, Xiaoyu Wang, Hong Liwang, Minghui Chen, Ning Fan, Xuwei Wang, Xianbin School of Future Science and Engineering Soochow University Suzhou215006 China School of Computing Technologies RMIT University MelbourneVIC Australia Department of Control Science and Engineering The National Key Laboratory of Autonomous Intelligent Unmanned Systems Tongji University Shanghai201804 China Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Tongji University Shanghai201804 China Qingdao China College of Computer and Information Sciences Fujian Agriculture and Forestry University Fuzhou350002 China Department of Electrical and Computer Engineering Western University ON Canada

Edge inference (EI) is a key solution to address the growing challenges of delayed response times, limited scalability, and privacy concerns in cloud-based Deep Neural Network (DNN) inference. However, deploying DNN models on resource-constrained edge devices faces more severe challenges, such as model storage limitations, dynamic service requests, and privacy risks. This paper proposes a novel framework for privacy-aware joint DNN model deployment and partition optimization to minimize long-term average inference delay under resource and privacy constraints. Specifically, the problem is formulated as a complex optimization problem considering model deployment, user-server association, and model partition strategies. To handle the NP-hardness and future uncertainties, a Lyapunov-based approach is introduced to transform the long-term optimization into a single-time-slot problem, ensuring system performance. Additionally, a coalition formation game model is proposed for edge server association, and a greedy-based algorithm is developed for model deployment within each coalition to efficiently solve the problem. Extensive simulations show that the proposed algorithms effectively reduce inference delay while satisfying privacy constraints, outperforming baseline approaches in various scenarios. Copyright © 2025, The Authors. All rights reserved.

关键词： Differential privacy

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Critical Analysis and Evaluation of Current Digital Healthcare Solutions 23

Critical Analysis and Evaluation of Current Digital Healthca...

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23rd International Conference on Control systems and computer science Technologies, CSCS 2021

作者： Ianculescu, Marilena Alexandru, Adriana Pop, Florin Systems and Applications for Society Department National Institute for RandD in Informatics Bucharest Romania Doctoral School of Automatic Control and Computers University Politehnica of Bucharest Bucharest Romania Department of Computer Science and Engineering University Politehnica of Bucharest Bucharest Romania

ISBN: (纸本)9781665439398

Digitalization of health, healthcare and healthcare delivery unequivocal have positive consequences contributing to health system goals, but they also might have some negative ones which have to be avoided or mitigated. The scope of this paper is to introduce the necessity of the assessment of Health Technology Assessment (HTA) and to identify the most appropriate and important criteria needed for the evaluation and comparison of functional digital healthcare solutions. It aims to outline consistent criteria against which digital health technologies and services can be evaluated during the prototyping phase, but also during long-term operation. © 2021 IEEE.

关键词： Health care

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