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An enhanced scheme for mutual authentication for healthcare services

Digital Communications and Networks 2022年第2期8卷 150-161页

作者： Salman Shamshad Muhammad Faizan Ayub Khalid Mahmood Saru Kumari Shehzad Ashraf Chaudhry Chien-Ming Chen Department of Computer Science COMSATS University IslamabadSahiwal Campus57000Pakistan Department of Mathematics Ch.Charan Singh UniversityMeerutUttar Pradesh250004India Department of Computer Engineering Faculty of Engineering and ArchitectureIstanbul Gelisim UniversityIstanbulTurkey College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China Riphah School of Computing&Innovation(RSCI) Riphah International UniversityLahore CampusLahore 55150Pakistan Department of Mathematics University of Padua35131 PaduaItaly

With the advent of state-of-art technologies,the Telecare Medicine Information System(TMIS)now offers fast and convenient healthcare services to patients at their ***,this architecture engenders new risks and challenges to patients'and the server's confidentiality,integrity and *** order to avoid any resource abuse and malicious attack,employing an authentication scheme is widely considered as the most effective approach for the TMIS to verify the legitimacy of patients and the ***,several authentication protocols have been proposed to this *** recently,Chaudhry et *** that there are vulnerabilities of impersonation attacks in Islam et al.'s ***,they introduced an improved protocol to mitigate those security ***,Qiu et *** that these schemes are vulnerable to the man-in-the-middle,impersonation and offline password guessing ***,they introduced an improved scheme based on the fuzzy verifier techniques,which overcome all the security flaws of Chaudhry et al.'s ***,there are still some security flaws in Qiu et al.'s *** this article,we prove that Qiu et al.'s protocol has an incorrect notion of perfect user anonymity and is vulnerable to user impersonation ***,we introduce an improved protocol for authentication,which reduces all the security flaws of Qiu et al.'s *** also make a comparison of our protocol with related protocols,which shows that our introduced protocol is more secure and efficient than previous protocols.

关键词： Authentication protocol Security protocol Anonymous protocol Impersonation attack TMIS

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Optimal Design of Heliostat Field Using Random Search Algorithm 6

Optimal Design of Heliostat Field Using Random Search Algori...

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6th IEEE International Conference on Power, Intelligent Computing and Systems, ICPICS 2024

作者： Mou, Chenglin Wu, Kaiqi Du, Pengcheng Yang, Kaibo School of Civil Engineering and Architecture Henan University of Science and Technology Department of Engineering Mechanics Luoyang471099 China School of Information Engineering Henan University of Science and Technology Department of Computer Science and Technology Luoyang471099 China School of Information Engineering Henan University of Science and Technology Department of Automation Luoyang471099 China School of International Education Henan University of Science and Technology Department of Civil Engineering Luoyang471099 China

ISBN: (纸本)9798350374315

Tower solar thermal power generation is a new type of low-carbon and environmentally friendly clean energy technology. In this paper, a single-objective optimization model is established with the maximum annual average thermal output per unit mirror area of the heliostat field as the objective function. By optimizing the heliostat width, heliostat height and heliostat coordinate, the annual average output thermal power can be maximized while meeting the rated power. The heliostat field is distributed in a circular shape. As the seasons change, the light intensity received by the heliostat field is uneven, so the optical efficiency of the heliostats is also different. We simplify the heliostat field into S area (area with higher optical efficiency) and W area (area with lower optical efficiency) based on the optical efficiency distribution of heliostats. At different locations, According to the heliostat width, heliostat height and heliostat coordinate generated by random functions, substituting these parameters into the efficiency model in this paper, and the random search algorithm is used to traverse the maximum annual output thermal power and the corresponding heliostat parameters. In the shadow occlusion loss model, we have grid to set up the heliostat and use the coordinate conversion to solve the shadow occlusion loss efficiency. © 2024 IEEE.

关键词： Heliostats (instruments)

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An efficient computational procedure to solve the biological nonlinear Leptospirosis model using the genetic algorithms

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Soft Computing 2024年第1期28卷 607-625页

作者： Sabir, Zulqurnain Ali, Mohamed R. Raja, Muhammad Asif Zahoor Sadat, R. Department of Mathematics and Statistics Hazara University Mansehra Pakistan Faculty of Engineering and Technology Future University in Egypt New Cairo11835 Egypt Basic Engineering Science Department Benha Faculty of Engineering Benha University Banha Egypt Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan Department of Mathematics Zagazig Faculty of Engineering Zagazig University Zagazig Egypt Department of Computer Science and Mathematics Lebanese American University Beirut Lebanon

The purpose of this work is to present the numerical solutions of the nonlinear mathematical Leptospirosis disease (LD) model using the computational performances of the artificial neural networks (ANNs) along with the optimization procedures based on the global search genetic algorithm (GA) and local search active-set algorithm (ASA) scheme, i.e., ANNs-GA-ASA. LD is a zoonotic disease that is occurring in the whole world, obtained by rodents that become the reason of death in the people. The LD model based on the susceptible-infected-recovered, i.e., SIR, is used to solve the mechanisms of disease spread. The optimization of an error-based fitness function, which is constructed through the differential mathematical system using the hybrid computing proficiencies of the ANNs-GA-ASA for solving the LD model. The stochastic ANNs-GA-ASA procedures are applied to the LD model to authenticate the precision, exactness, reliability and competence of the ANNs-GA-ASA. The obtained results using the ANNs-GA-ASA of the LD model will be compared using the Runge–Kutta scheme, which authenticate the importance of the ANNs-GA-ASA. Furthermore, statistical analysis through different actions for the LD model approve the convergence and precision of the ANNs-GA-ASA. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Neural networks

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An enhanced strategy for minority class detection using bidirectional GRU employing penalized cross-entropy and self-attention mechanisms for imbalance network traffic

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European Physical Journal Plus 2024年第6期139卷 1-24页

作者： Abbasi, Asima Akber Zameer, Aneela Raja, Muhammad Asif Zahoor Department of Computer and Information Sciences Pakistan Institute of Engineering and Applied Sciences (PIEAS) Islamabad Nilore 45650 Pakistan Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou 64002 Taiwan

The emergence of the Internet as a global communication tool has brought significant transformations across various aspects of our everyday lives. However, it has also resulted in new technical challenges, such as the risk of personal information exposure, network congestion, and limited availability of network addresses. The cybersecurity field has greatly benefited from the implementation of artificial intelligence and machine learning to model network activity and improve the detection of potential threats. Intrusion detection systems are specifically unable to tackle the challenges of high-class imbalance and the varying significance of misclassifications specifically for minority classes. The proposed SABiG (CPL) model targets the high dimensional imbalance data set CIC-IDS2017 by employing the evolutionary computing framework of genetic algorithms utilizing random forest GA(RF) for optimal feature selection. For attack classification, a powerful deep learning technique of bidirectional gated recurrent unit (Bi-GRU) with a self-attention mechanism is exploited. Our proposed model features a custom penalized cross entropy strategy by assigning three penalty values according to class imbalanced ratios to address the misclassification issue. The experimental results have demonstrated enhanced detection accuracy for all classes in general and minority classes in particular, such as infiltration, and web attacks. Comparison with the existing techniques in terms of all standard performance indices further evaluates our proposed intrusion detection system for imbalanced data. The generalization of the proposed model is evaluated with the recent CIC-CSE-IDS2018 web attack dataset. © The Author(s), under exclusive licence to Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2024.

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Novel Design of Grey Wolf Optimization Heuristics for High Resolution Direction of Arrival Estimation in Acoustic Plane Waves

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Wireless Personal Communications 2023年第4期128卷 2507-2529页

作者： Ahmed, Nauman Wang, Huigang Raja, Muhammad Asif Zahoor Ali, Wasiq Kashif, Muhammad Zaman, Fawad School of Marine Science and Technology Northwestern Polytechnical University Xi’an China Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Douliou Yunlin64002 Taiwan COMSATS University Islamabad Attock Campus Attock Pakistan Department of Electrical and Computer Engineering COMSATS University Islamabad Islamabad Pakistan

High resolution direction of arrival estimation for closely spaced target in low signal to noise ration (SNR) is an active research area in underwater acoustic and SONAR signal processing The resolution performance of classical/traditional subspace based algorithms including MUSIC and ESPRIT procedures degraded under sever scenarios of low signal to noise ratio (SNR), less number of samples/snapshots and short distant targets. In this work, optimization courage of swarming intelligence of grey wolf optimization (GWO) is carried out for viable DOA estimation in the challenging instances of worst underwater scenarios using a uniform linear array (ULA). The high resolution DOA for short distant emitters is achieved using less number of samples vitally with GWO by inspecting the global minima of the highly nonlinear objective model of ULA having multi local minimas. The performance is analyzed for different number of targets employing estimation accuracy, resolution certainty, variability analysis, frequency distribution of RMSE, robustness against different SNR of additive-white Gaussian measurement noise and comparative studies with MUSIC and Particle Swarm Optimization PSO with the validation strength of Cramer Rao bound reveals the valuation of the scheme. © 2022, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Direction of arrival

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AGV indoor localization: a high fidelity positioning and map building solution based on drawstring displacement sensors

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Journal of Ambient Intelligence and Humanized Computing 2024年第4期15卷 2277-2293页

作者： Wang, Shih-Yuan Li, Che-Ming Liong, Sze-Teng Sheng, Yu-Ting Huang, Yen-Chang Liong, Gen-Bing Gan, Yee-Siang Graduate Institute of Architecture National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Electronic Engineering Feng Chia University Taichung40724 Taiwan School of Architecture Feng Chia University Taichung40724 Taiwan Department of Applied Mathematics National University of Tainan Tainan Taiwan Faculty of Computer Science & amp Information Technology University of Malaya Kuala Lumpur50603 Malaysia

The rapid evolution of artificial intelligence (AI) and robotics technologies are bringing drastic changes to society and industry in these recent years. The impressive progress in facilitating smart manufacturing in this era of Industry 4.0 has made our lives much more convenient than ever before. Despite the greater reliability and stability of the robotic system, there are several challenges to overcome such as the restrictions of scenes, obstacles, and hardware specifications. Since a high precision positioning algorithm is of paramount importance in devising a mobile robot, the research in developing simultaneous localization and mapping has been garnered immerse attention especially from domains of the computer vision and autonomous robots. In this paper, a novel method is presented to significantly enhance the positioning precision of indoor unmanned guided vehicles. The approach involves several steps, including setting up hardware configurations and collecting relevant data by installing necessary devices and system packages within the robot operating system (ROS). Trilateration is employed to determine the relative position of the mobile robot using distance measurements. Coordinate transformation is then conducted to convert the collected input data of relative distances and orientations. Trajectory paths are obtained, and occupancy maps are constructed to estimate the resulting trajectory and generate a 2D grid map. Indoor localization and mapping are achieved using three drawstring displacement sensors along with orientation information from an Inertial Measurement Unit (IMU). The proposed method is extensively evaluated through experimentation on predefined navigation paths, and its performance is compared to state-of-the-art methods such as RealSense T265, Hector SLAM, and wheel odometry. The results show that the proposed method exhibits compelling performance in both mean error and occupancy map construction. Ultimately, the findings reported herein

关键词： Mobile robots

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ThickV-Stain: Unprocessed Thick Tissues Virtual Staining for Rapid Intraoperative Histology 7

ThickV-Stain: Unprocessed Thick Tissues Virtual Staining for...

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7th International Conference on Medical Imaging with Deep Learning, MIDL 2024

作者： Shi, Lulin Hou, Xingzhong Wong, Ivy H.M. Chan, Simon C.K. Chen, Zhenghui Lo, Claudia T.K. Wong, Terence T.W. Translational and Advanced Bioimaging Laboratory Department of Chemical and Biological Engineering The Hong Kong University of Science and Technology Hong Kong State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China

Virtual staining has shown great promise in realizing a rapid and low-cost clinical alternative for pathological examinations, eliminating the need for chemical reagents and laborious staining procedures. However, most of the previous studies mainly focus on thin slice samples, which still require tissue sectioning and are unsuitable for intraoperative use. In this paper, we propose a multi-scale model to virtually stain label-free and slide-free biological tissues, allowing hematoxylin- and eosin- (H&E) staining generation in less than a minute for an image with 100 million pixels. We name this ThickV-Stain model, specifically developed to virtually stain intricated and unprocessed thick tissues. We harness the ability of a multi-scale network to encourage the model to capture multiple-level micromorphological characteristics from low-resolution images. Experimental results highlight the advantages of our method for virtual staining on unprocessed thick samples. We also show the effectiveness of ThickV-Stain on thin sections, showing generalizability to other clinical workflows. The proposed method enables us to obtain virtually stained images from unstained samples within minutes and can be seamlessly integrated with downstream pathological analysis tasks, providing an efficient alternative scheme for intraoperative assessment as well as general pathological examination. © 2024 CC-BY 4.0, L. Shi, X. Hou, I.H.M. Wong, S.C.K. Chan, Z. Chen, C.T.K. Lo & T.T.W. Wong.

关键词： Tissue

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Optimal Load Forecasting Model for Peer-to-Peer Energy Trading in Smart Grids

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computers, Materials & Continua 2022年第1期70卷 1053-1067页

作者： Lijo Jacob Varghese K.Dhayalini Suma Sira Jacob Ihsan Ali Abdelzahir Abdelmaboud Taiseer Abdalla Elfadil Eisa Department of Electrical and Electronics Engineering Christian College of Engineering and TechnologyOddanchatram624619India Department of Electrical and Electronics Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Department of Computer Science and Engineering Christian College of Engineering and TechnologyOddanchatram624619India Department of Computer System and Technology Faculty of Computer Science and Information TechnologyUniversity of Malaya50603Kuala LumpurMalaysia Department of Information Systems King Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Information Systems-Girls Section King Khalid UniversityMahayil62529Saudi Arabia

Peer-to-Peer(P2P)electricity trading is a significant research area that offers maximum fulfilment for both prosumer and *** also decreases the quantity of line loss incurred in Smart Grid(SG).But,uncertainities in demand and supply of the electricity might lead to instability in P2P market for both prosumer and *** recent times,numerous Machine Learning(ML)-enabled load predictive techniques have been developed,while most of the existing studies did not consider its implicit features,optimal parameter selection,and prediction *** order to overcome fulfill this research gap,the current research paper presents a new Multi-Objective Grasshopper Optimisation Algorithm(MOGOA)with Deep Extreme Learning Machine(DELM)-based short-term load predictive technique i.e.,MOGOA-DELM model for P2P Energy Trading(ET)in *** proposed MOGOA-DELM model involves four distinct stages of operations namely,data cleaning,Feature Selection(FS),prediction,and parameter *** addition,MOGOA-based FS technique is utilized in the selection of optimum subset of ***,DELM-based predictive model is also applied in forecasting the load *** proposed MOGOA model is also applied in FS and the selection of optimalDELM parameters to improve the predictive *** inspect the effectual outcome of the proposed MOGOA-DELM model,a series of simulations was performed using UK Smart Meter *** the experimentation procedure,the proposed model achieved the highest accuracy of 85.80%and the results established the superiority of the proposed model in predicting the testing data.

关键词： Peer to Peer energy trade smart grid load forecasting machine learning feature selection

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Parameter estimation of nonlinear systems: dwarf mongoose optimization algorithm with key term separation principle

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Journal of Ambient Intelligence and Humanized Computing 2023年第12期14卷 16921-16931页

作者： Mehmood, Khizer Chaudhary, Naveed Ishtiaq Khan, Zeshan Aslam Cheema, Khalid Mehmood Raja, Muhammad Asif Zahoor Department of Electrical and Computer Engineering International Islamic University Islamabad44000 Pakistan Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan Department of Electronic Engineering Fatima Jinnah Women University Rawalpindi46000 Pakistan

In recent years, swarm intelligence-based metaheuristics are extensively explored for optimizing challenging and stiff nonlinear problems. This research explores the identification of the input nonlinear output error (NOE) model through a swarm intelligence-based metaheuristic called dwarf mongoose optimization algorithm (DMOA). Further, the key term separation method (KTSM) based identification model is presented to address the issue of over-parameterization. The DMOA performance is deeply examined for various generations, population sizes, and signal-to-noise ratios. The comparative analyses of the DMOA with the recent counterpart through observations of non-parametric statistical tests confirmed the effectiveness of the DMOA for NOE identification. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Nonlinear systems

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Predicting the friction angle of clays using a multi-layer perceptron neural network enhanced by yeo-johnson transformation and coral reefs optimization

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Journal of Rock Mechanics and Geotechnical Engineering 2024年第10期16卷 3982-4003页

作者： Libing Yang Trung Nguyen-Thoi Trung-Tin Tran College of Civil Engineering and Architecture Hunan Institute of Science and TechnologyYueyang414006China Laboratory for Applied and Industrial Mathematics Institute for Computational Science and Artificial IntelligenceVan Lang UniversityHo Chi Minh CityViet Nam Faculty of Mechanical-Electrical and Computer Engineering School of TechnologyVan Lang UniversityHo Chi Minh CityViet Nam Department of Information Technology Greenwich VietnamFPT UniversityDa Nang550000Viet Nam

The accurate prediction of the friction angle of clays is crucial for assessing slope stability in engineering *** study addresses the importance of estimating the friction angle and presents the development of four soft computing models:YJ-FPA-MLPnet,YJ-CRO-MLPnet,YJ-ACOC-MLPnet,and *** of all,the Yeo-Johnson(YJ)transformation technique was used to stabilize the variance of data and make it more suitable for parametric statistical models that assume normality and equal *** technique is expected to improve the accuracy of friction angle prediction *** friction angle prediction models then utilized multi-layer perceptron neural networks(MLPnet)and metaheuristic optimization algorithms to further enhance performance,including flower pollination algorithm(FPA),coral reefs optimization(CRO),ant colony optimization continuous(ACOC),and cuckoo search algorithm(CSA).The prediction models without the YJ technique,***-MLPnet,CRO-MLPnet,ACOC-MLPnet,and CSA-MLPnet,were then compared to those with the YJ technique,***-FPA-MLPnet,YJ-CRO-MLPnet,YJ-ACOC-MLPnet,and *** these,the YJ-CRO-MLPnet model demonstrated superior reliability,achieving an accuracy of up to 83%in predicting the friction angle of clay in practical engineering *** improvement is significant,as it represents an increase from 1.3%to approximately 20%compared to the models that did not utilize the YJ transformation technique.

关键词： Natural hazards Slope stability Friction angle Clay Soft computing models Geotechnical engineering

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