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International Conference on Data science and Network engineering, ICDSNE 2023

作者： Dewan, Ritu Nagpal, Tapsi Ahmad, Sharik Rana, Arun Kumar Islam, Sardar M. N. Department of Computer Science and Engineering Lingayas Vidyapeeth Faridabad India Department of Computer Science and Engineering Galgotias College of Engineering & Technology Gr. Noida India SILC Decision Sciences and Modelling Program Victoria University Victoria Australia

ISBN: (纸本)9789819967544

Thanks to constantly advancing technology, the world is changing rapidly. One such idea that has contributed to the reality of automation is the Internet of Things (IoT). IoT links various non-living objects to the internet and enables them to communicate with their local network to automate processes and simplify people’s lives. The IoT’s potential needs to be completely realised despite the enormous efforts of standards, organisations, coalitions, businesses, academics and others. A number of problems remain. The enabling technology, applications and business models, as well as the social and environmental repercussions, should all be taken into consideration while analysing these difficulties. This article’s emphasis is on unresolved issues and challenges from a technological perspective. The main objective is to provide a thorough evaluation of IoT in terms of energy and security, as well as unresolved problems and obstacles that need further study. We provide some perspectives on some new concepts to help future research. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Internet of things

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Federated Machine Learning Based Fetal Health Prediction Empowered with Bio-Signal Cardiotocography

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computers, Materials & Continua 2024年第3期78卷 3303-3321页

作者： Muhammad Umar Nasir Omar Kassem Khalil Karamath Ateeq Bassam SaleemAllah Almogadwy Muhammad Adnan Khan Muhammad Hasnain Azam Khan Muhammad Adnan Department of Computer Sciences Bahria University Lahore CampusLahore54000Pakistan Faculty of Information Technology Liwa CollegeAbu Dhabi00000United Arab Emirates Department of Computing Skyline University CollegeSharjah1797United Arab Emirates Department of Computer Science Taibah UniversityMedina42353Saudi Arabia Riphah School of Computing and Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Department of Engineering and Development Dubizzle LabsLahore54000Pakistan Department of Software Faculty of Artificial Intelligence and SoftwareGachon UniversitySeongnam-si13120Korea

Cardiotocography measures the fetal heart rate in the fetus during pregnancy to ensure physical health because cardiotocography gives data about fetal heart rate and uterine shrinkages which is very beneficial to detect whether the fetus is normal or suspect or *** cardiotocography measures infer wrongly and give wrong predictions because of human *** traditional way of reading the cardiotocography measures is the time taken and belongs to numerous human errors as *** condition is very important to measure at numerous stages and give proper medications to the fetus for its *** the current period Machine learning(ML)is a well-known classification strategy used in the biomedical field on various issues because ML is very fast and gives appropriate results that are better than traditional *** techniques play a pivotal role in detecting fetal disease in its early *** research article uses Federated machine learning(FML)and ML techniques to classify the condition of the *** study proposed a model for the detection of bio-signal cardiotocography that uses FML and ML techniques to train and test the ***,the proposed model of FML used numerous data preprocessing techniques to overcome data deficiency and achieves 99.06%and 0.94%of prediction accuracy and misprediction rate,respectively,and parallel the proposed model applying K-nearest neighbor(KNN)and achieves 82.93%and 17.07%of prediction accuracy and misprediction accuracy,***,by comparing both models FML outperformed the KNN technique and achieved the best and most appropriate prediction results as compared with previous studies the proposed study achieves the best and most accurate results.

关键词： Cardiotocography ML FML fetal disease bio-signal

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Augmented electric eel foraging optimization algorithm for feature selection with high-dimensional biological and medical diagnosis

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Neural Computing and Applications 2024年第35期36卷 22171-22221页

作者： Al-Betar, Mohammed Azmi Braik, Malik Sh. Mohamed, Elfadil A. Awadallah, Mohammed A. Nasor, Mohamed College of Engineering and Information Technology Ajman University Ajman United Arab Emirates Department of Information Technology Al-Huson University College Al-Balqa Applied University Irbid Jordan Department of Computer Science Al-Balqa Applied University Al-Salt19117 Jordan Department of Computer Science Al-Aqsa University P.O. Box 4051 Gaza Palestine Ajman University Ajman United Arab Emirates

This paper explores the importance of the electric eel foraging optimization (EEFO) algorithm in addressing feature selection (FS) problems, with the aim of ameliorating the practical benefit of FS in real-world applications. The use of EEFO to solve FS problems props our goal of providing clean and useful datasets that provide robust effectiveness for use in classification and clustering tasks. High-dimensional feature selection problems (HFSPs) are more common nowadays yet intricate where they contain a large number of features. Hence, the vast number of features in them should be carefully selected in order to determine the optimal subset of features. As the basic EEFO algorithm experiences premature convergence, there is a need to enhance its global and local search capabilities when applied in the field of FS. In order to tackle such issues, a binary augmented EEFO (BAEEFO) algorithm was developed and proposed for HFSPs. The following strategies were integrated into the mathematical model of the original EEFO algorithm to create BAEEFO: (1) resting behavior with nonlinear coefficient;(2) weight coefficient and confidence effect in the hunting process;(3) spiral search strategy;and (4) Gaussian mutation and random perturbations when the algorithm update is stagnant. Experimental findings confirm the effectiveness of the proposed BAEEFO method on 23 HFSPs gathered from the UCI repository, recording up to a 10% accuracy increment over the basic BEEFO algorithm. In most test cases, BAEEFO outperformed its competitors in classification accuracy rates and outperformed BEEFO in 90% of the datasets used. Thereby, BAEEFO has demonstrated strong competitiveness in terms of fitness scores and classification accuracy. When compared to its competitors, BAEEFO produced superior reduction rates with the fewest number of features selected. The findings in this research underscore the critical need for FS to combat the curse of dimensionality concerns and find highly useful fea

关键词： Heuristic algorithms

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Toward Symptom Assessment Guided Symptom Investigation and Disease Diagnosis

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第6期4卷 1752-1766页

作者： Tiwari, Abhisek Raj, Rishav Saha, Sriparna Bhattacharyya, Pushpak Tiwari, Sarbajeet Dhar, Minakshi Indian Institute of Technology Department of Computer Science and Engineering Patna800013 India Indian Institute of Technology Department of Computer Science and Engineering Bombay400076 India Midnapore Homeopathic Medical College and Hospital Midnapore721101 India All India Institute of Medical Sciences Department of Medicine Rishikesh249203 India

Automatic disease diagnosis has gained immense popularity and demand over the past few years, and it is emerging as an effective diagnostic assistant to doctors. Diagnosis assistants assist clinicians in conducting a thorough symptom investigation and identifying possible diseases. Doctors correctly diagnose patients by observing only a few symptoms in most cases, even though the diagnosed disease has numerous symptoms. Also, some common symptoms, such as fever and headache, usually emerge due to other symptoms, which do not play a major role in identifying suffering diseases. In this work, we investigate the role of symptom importance in disease diagnosis through several feature engineering techniques and propose a novel symptom assessment guided symptom investigation and disease diagnosis (SA-SIDD) assistant using hierarchical reinforcement learning (HRL). The proposed SA-SIDD assistant first collects an adequate set of symptoms/sign information through conversing with users and then diagnoses a disease based on the extracted symptoms. We incorporated a symptom assessment module with the diagnosis framework that evaluates the relevance of current inspected symptom at each turn and reinforces the assistant to investigate distinctive and context-aligned symptoms using an assessment critic. The proposed methodology outperforms the state-of-the-art method, HRL, on two publicly available datasets, which firmly establishes the crucial role of symptom importance in disease diagnosis and the need for the proposed symptom assessment incorporated disease diagnosis framework. Furthermore, we have also conducted a human evaluation, revealing that the diagnosis method greatly enhances end-user satisfaction because of context-aligned relevant and minimal symptom investigation. © 2020 IEEE.

关键词： Reinforcement learning

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Prescreening of large-effect markers with multiple strategies improves the accuracy of genomic prediction

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Journal of Integrative Agriculture 2024年第5期23卷 1634-1643页

作者： Keanning Li Bingxing An Mang Liang Tianpeng Chang Tianyu Deng Lili Du Sheng Cao Yueying Du Hongyan Li Lingyang Xu Lupei Zhang Xue Gao Junya LI Huijiang Gao Laboratory of Molecular Biology and Bovine Breeding Institute of Animal SciencesChinese Academy of Agricultural SciencesBeijing 100193China Shaanxi Key Laboratory of Molecular Biology for Agriculture College of Animal Science and TechnologyNorthwest A&F UniversityYangling712100China College of Computer and Information Engineering Tianjin Agricultural UniversityTianjin 300384China College of Animal Science and Technology Qingdao Agricultural UniversityQingdao 266109China Tongliao Animal Agriculture Development Service Center Tongliao 028000China

Presently,integrating multi-omics information into a prediction model has become a ameliorate strategy for genomic selection to improve genomic prediction ***,we set the genomic and transcriptomic data as the training population data,using BSLMM,TWAS,and eQTL mapping to prescreen features according to |β_(b)|>0,top 1%of phenotypic variation explained(PVE),expression-associated single nucleotide polymorphisms(eSNPs),and egenes(false discovery rate(FDR)<0.01),where these loci were set as extra fixed effects(named GBLUP-Fix)and random effects(GFBLUP)to improve the prediction accuracy in the validation population,*** results suggested that both GBLUP-Fix and GFBLUP models could improve the accuracy of longissimus dorsi muscle(LDM),water holding capacity(WHC),shear force(SF),and pH in Huaxi cattle on average from 2.14 to 8.69%,especially the improvement of GFBLUP-TWAS over GBLUP was 13.66%for *** methods also captured more genetic variance than *** study confirmed that multi-omics-assisted large-effects loci prescreening could improve the accuracyofgenomic prediction.

关键词： multi-omics data features prescreening eQTL mapping Huaxi cattle genomic selection

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DDT-Net:Deep Detail Tracking Network for Image Tampering Detection

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computers, Materials & Continua 2025年第5期83卷 3451-3469页

作者： Jim Wong Zhaoxiang Zang Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering China Three Gorges UniversityYichang443002China College of Computer and Information Technology ChinaThree Gorges UniversityYichang443002China Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming650504China

In the field of image forensics,image tampering detection is a critical and challenging *** methods based on manually designed feature extraction typically focus on a specific type of tampering operation,which limits their effectiveness in complex scenarios involving multiple forms of *** deep learningbasedmethods offer the advantage of automatic feature learning,current approaches still require further improvements in terms of detection accuracy and computational *** address these challenges,this study applies the UNet 3+model to image tampering detection and proposes a hybrid framework,referred to as DDT-Net(Deep Detail Tracking Network),which integrates deep learning with traditional detection *** contrast to traditional additive methods,this approach innovatively applies amultiplicative fusion technique during downsampling,effectively combining the deep learning feature maps at each layer with those generated by the Bayar noise *** design enables noise residual features to guide the learning of semantic features more precisely and efficiently,thus facilitating comprehensive feature-level ***,by leveraging the complementary strengths of deep networks in capturing large-scale semantic manipulations and traditional algorithms’proficiency in detecting fine-grained local traces,the method significantly enhances the accuracy and robustness of tampered region *** with other approaches,the proposed method achieves an F1 score improvement exceeding 30% on the DEFACTO and DIS25k *** addition,it has been extensively validated on other datasets,including CASIA and *** results demonstrate that this method achieves outstanding performance across various types of image tampering detection tasks.

关键词： Image forensics image tampering detection image manipulation detection noise flow Bayar

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Time-Synchronized Formation Control of Unmanned Surface Vehicles

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年第10期9卷 1-9页

作者： Wang, Duansong Ge, Shuzhi Sam Liang, Xiaoling Li, Dongyu School of Electrical and Photoelectronic Engineering West Anhui University LuAn China Department of Electrical and Computer Engineering National University of Singapore Singapore College of Marine Engineering Dalian Maritime University Dalian China School of Cyber Science and Technology Beihang University Beijing China

Time-synchronization (TS) formation control for unmanned surface vehicles (USVs) presents several advantages, including precise execution of tasks, broadened combat capabilities, and improved information fusion quality. To achieve this performance, a time-synchronized formation control method is presented that takes into account direct topology, external disturbances, and system uncertainties (EDSU). In contrast to prior formation control strategies, we introduce the formalized time-synchronized formation control framework, where all state components of the formation system concurrently converge to the equilibrium point at a uniform time constant, independently of their initial states. To counteract the EDSU, a fixed-time disturbance observer is designed to guarantee the convergence of all observer error components to zero. System stability is corroborated through the application of Lyapunov-like theory. Simulations and comparative experiments on three USVs are conducted to demonstrate the proposed method's superiority. IEEE

关键词： Synchronization

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A Secure Three-Party Authenticated Key Exchange Protocol for Social Networks

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computers, Materials & Continua 2022年第6期71卷 6293-6305页

作者： Vivek Kumar Sinha Divya Anand Fahd S.Alharithi Ahmed H.Almulihi Department of Computer Science and Engineering Lovely Professional UniversityPhagwaraPunjab144411India Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

The 3PAKE(Three-Party Authenticated Key Exchange)protocol is a valuable cryptographic method that offers safe communication and permits two diverse parties to consent to a new safe meeting code using the trusted *** have been explored numerous 3PAKE protocols earlier to create a protected meeting code between users employing the trusted ***,existing modified 3PAKE protocols have numerous drawbacks and are incapable to provide desired secrecy against diverse attacks such as manin-the-middle,brute-force attacks,and many others in social *** this article,the authors proposed an improved as well as safe 3PAKE protocol based on the hash function and the symmetric encryption for the social *** authors utilized a well-acknowledged AVISPA tool to provide security verification of the proposed 3PAKE technique,and findings show that our proposed protocol is safer in opposition to active as well as passive attacks namely the brute-force,man-in-the-middle,parallel attack,and many ***,compared to other similar schemes,the proposed protocol is built with a reduced computing cost as our proposed protocol consumes less time in execution and offers high secrecy in the social networks with improved *** a result,this verified scheme is more efficient as well as feasible for implementation in the social networks in comparison to previous security *** multifarious authors carried out extensive research on 3PAKE protocols to offer safe communication,still there are vital opportunities to explore and implement novel improved protocols for higher safety in the social networks and mobile commerce environment in the future in opposition to diverse active as well as passive attacks.

关键词： AVISPA tool 3PAKE protocol hash function symmetric encryption social networks

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Improving Pedestrian Detection in Low-Resolution Videos Using a HOG-SVM Framework 3rd

Improving Pedestrian Detection in Low-Resolution Videos Usin...

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3rd International Conference on Machine Learning, Cloud Computing and Intelligent Mining, MLCCIM 2024

作者： Singh, Prem Narayan Verma, Sonia Dasi, Shivakrishna Alwan, Adil Abbas Kaur, Amandeep Alabdeli, Haider Greater Noida201306 India Department of Computer Science ABES Engineering College Ghaziabad201204 India NIMS School of Computing Science and Artificial Intelligence NIMS University Rajasthan Rajasthan Jaipur India College of Technical Engineering Dhi Qar National University of Science and Technology Nasiriyah64001 Iraq Department of Computer Science Engineering Chandigarh Engineering College Chandigarh Group of Colleges Jhanjeri Punjab Mohali140307 India The Islamic University Najaf Iraq

ISBN: (纸本)9789819624676

This study tackles the intricate challenge of pedestrian detection in low-resolution aerial videos, where conventional methods relying on facial features struggle due to image blurriness and human variability. Our innovative approach shifts the focus from appearance to movement patterns, analyzing short video sequences (approximately half a second). We address two primary tasks: video stabilization and identification of moving objects. By utilizing specialized features capturing both spatial and temporal information, we effectively classify these moving objects as pedestrians. Across diverse and challenging datasets, our method demonstrates superiority over traditional approaches, particularly in low-resolution scenarios. It excels by achieving higher detection accuracy and minimizing false positives. The proposed approach is an ideal solution for real-world applications, especially in situations where video quality is compromised, such as surveillance footage and drone video processing. The significance of our method lies in its ability to adapt to challenging environments, providing robust pedestrian detection in low-resolution aerial footage. With enhanced accuracy and reduced false positives, our approach offers practical advantages for applications requiring reliable performance in compromised video quality scenarios, including surveillance and drone video processing. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Drones

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Research on Multi-Blockchain Electronic Archives Sharing Model

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computers, Materials & Continua 2023年第9期76卷 3921-3931页

作者： Fang Yu Wenbin Bi Ning Cao Jun Luo Diantang An Liqiang Ding Russell Higgs School of Information Engineering Qingdao Binhai UniversityQingdao266555China School of Computer and Software Dalian Neusoft University of InformationDalian116023China School of Internet of Things and Software Technology Wuxi Vocational College of Science and TechnologyWuxi214028China Archives Management Department QingdaoWest Coast New Area ArchivesQingdao266427China Editorial Research Department QingdaoWest Coast New Area ArchivesQingdao266427China School of Mathematics and Statistics University College DublinDublinIreland

The purpose of introducing blockchain into electronic archives sharing and utilization is to break the information barrier between electronic archives sharing departments by relying on technologies such as smart contract and asymmetric *** at the problem of dynamic permission management in common access control methods,a new access control method based on smart contract under blockchain is proposed,which improves the intelligence level under blockchain ***,the Internet attribute access control model based on smart contract is *** the dynamic access of heterogeneous devices,the management contract,permission judgment contract and access control contract are designed;Secondly,the access object credit evaluation algorithm based on particle swarm optimization radial basis function(PSO-RBF)neural network is used to dynamically generate the access node credit threshold combined with the access policy,so as to realize the intelligent access right management ***,combined with the abovemodels and algorithms,the workflow of electronic archives sharing and utilization model of multi blockchain is *** experimental results show that the timeconsuming of the process increases linearly with the number of continuous access to electronic archives blocks,and the secure access control of sharing and utilization is feasible,secure and effective.

关键词： Sharing and utilization of electronic archives dynamic permission management PSO-RBF neural network credit evaluation

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