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Cloud-Assisted Secure and Cost-Effective Authenticated Solution for Remote Wearable Health Monitoring System

IEEE Transactions on Network Science and engineering 2023年第5期10卷 2710-2718页

作者： Mahmood, Khalid Obaidat, Mohammad S. Ghaffar, Zahid Alzahrani, Bander A. Shamshad, Salman Saleem, Muhammad Asad Hussain, Shafiq Lahore Campus Lahore55150 Pakistan University of Padua Department of Mathematics Padua35131 Italy University of Jordan KASIT Amman11942 Jordan University of Science and Technology Beijing100083 China Barani Institute of Sciences Department of Computer Science Sahiwal57000 Pakistan King Abdulaziz University Faculty of Computing and Information Technology Jeddah Saudi Arabia The University of Lahore Department of Software Engineering Lahore54590 Pakistan COMSATS University Islamabad Department of Computer Science Sahiwal Campus Sahiwal57000 Pakistan University of Sahiwal Department of Computer Science Sahiwal57000 Pakistan

Security and privacy are the leading solicitude in cloud computing since users have restricted privilege on the data maintained by distinct service providers at remote locations. The situation becomes more strenuous when a huge amount of data is sensed and produced by wearable devices as it is usually considered more sensitive and heterogeneous. The majority of existing authenticated solutions for such a challenging environment either suffer due to high computation and communication costs or due to the fact that they are susceptible to known cyber threats making them impractical for utilization. Therefore we introduced a cost-effective and secure authenticated scheme for the cloud-assisted remote wearable health monitoring system. Informal security proof is discussed to showcase the security strength of the proposed scheme against known security threats. After that a detailed performance analysis of the proposed scheme with related protocols reveals the fact that it can reduce the computation and communication costs by more than 37% and 40%, respectively. Moreover, our scheme offers some added security features like physical and desynchronization attacks prevention. © 2013 IEEE.

关键词： Authentication

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Scaling down to scale up: benchmarking single-machine graph processing frameworks in a hardware-constrained environment

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Computing 2025年第7期107卷 1-25页

作者： Loukil, Mariem Sfaxi, Lilia Robbana, Riadh Software Engineering Department Mediterranean Institute of Technology South Mediterranean University Tunis Tunisia LIPSIC Laboratory Faculty of Science of Tunis (FST) University of Tunis El Manar Tunis Tunisia Department of Computer Science Engineering and Mathematics INSAT University of Carthage Tunis Tunisia

Graph processing frameworks are pivotal in managing the increasing complexity and size of datasets in numerous computational fields. However, the performance of these frameworks can vary significantly depending on their design and the specific conditions under which they are tested. This paper presents a comprehensive benchmarking study of six single-machine graph processing frameworks: GraphChi, Ligra, X-stream, MMap, GridGraph, and GPOP across a variety of datasets and graph processing algorithms, and on a consumer-level machine. In the evaluation of the frameworks, several performance metrics were used, which are execution time, CPU, and memory usage, as well as qualitative metrics, such as ease of use and comprehensive documentation which contribute to determining each framework’s practical effectiveness. Our findings indicate that no single framework excels in all aspects; instead, each framework demonstrates strengths and weaknesses that are context-dependent. By evaluating frameworks in a unified environment rather than their original setups, the study explores which frameworks perform more effectively under comparable conditions.

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A Study of Synthetic Double Sampling np Chart Based on Median Run Length 3

A Study of Synthetic Double Sampling np Chart Based on Media...

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3rd International Conference on Artificial Intelligence and Data Sciences, AiDAS 2022

作者： Tuh, Moi Hua Kon, Cynthia Mui Lian Chua, Hong Siang Lau, Man Fai Hui Chang, Robin Yee Swinburne University of Technology Faculty of Engineering Computing and Science Sarawak Kuching Malaysia Universiti Teknologi Mara Faculty of Computer and Mathematical Sciences Sarawak Kota Samarahan Malaysia School of Software and Electrical Engineering Swinburne University of Technology Melbourne Australia Universiti Teknologi Mara Faculty of Applied Sciences Sarawak Kota Samarahan Malaysia

ISBN: (数字)9781665491648

ISBN: (纸本)9781665491648

The Markov chain method has been adopted in this work to evaluate the performance of the synthetic double sampling (SynDS) np chart, which combines the double sampling (DS) np sub-chart and the conforming run length (CRL) sub-chart. In the literature, the performance of the existing SynDS np chart is investigated using average run length (ARL). It is found that the skewness of the run length (RL) distribution varies depending on the size of shift in the process fraction nonconforming, from being significantly right-skewed when the process is in-control (IC) or small shift to being nearly symmetric when the process shift is large. As a result, the true performance of the chart may differ from what the ARL displays. Thus, median run length (MRL) is recommended to represent the central tendency associated with the varying shapes of the RL distribution in accordance to the process shift sizes. This work suggests an optimal design approach for SynDS np chart based on MRL to monitor the process fraction nonconforming, under zero-state and steady-state modes. © 2022 IEEE.

关键词： Control charts

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Resource provisioning in collaborative fog computing for multiple delay-sensitive users

Resource provisioning in collaborative fog computing for mul...

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作者： Lu, Shuaibing Wu, Jie Wang, Ning Duan, Yubin Liu, Haiming Zhang, Jiayue Fang, Juan Faculty of Information Technology Beijing University of Technology Beijing China Center for Networked Computing Temple University PhiladelphiaPA United States Department of Computer Science Rowan University GlassboroNJ United States School of Software Engineering Beijing Jiaotong University Beijing China

Fog computing is an emerging paradigm that supplies storage, computation, and networking resources between traditional cloud data centers and end devices. This article focuses on the resource provisioning problem in collaborative fog computing for multiple delay-sensitive users. Our goal is to implement a resource provisioning strategy for network operators to minimize the total monetary cost by considering the deadline and capacity constraints. Two scenarios are considered: unlimited-processor fog nodes (UPFN) and limited-processor fog nodes (LPFN). In either scenario, we prove that the resource provisioning problem is NP-hard. First, we consider the UPFN scenario that the processors of fog nodes are unlimited and users' requests can be ideally processed in parallel. Two algorithms are proposed which greedily delete fog nodes based on the local or global collaborative influences until there is no feasible provisioning to guarantee the deadline of users. Then we extend the resource provisioning problem to a more realistic and complicated scenario LPFN in which the scheduling delay cannot be ignored. Two types of tasks are considered. One is the arbitrarily divided tasks, and a near-optimal solution bounded by (Formula presented.) has been found. m is the number of fog nodes, and (Formula presented.) is the upper bound on the Lipschitz constant of the delay function. Another one is the application-driven tasks, and we propose a heuristic algorithm. Extensive experiments validate the efficiency of the proposed algorithms. © 2021 John Wiley & Sons, Ltd.

关键词： Fog

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Vibrating Particles System Algorithm for Solving Classification Problems

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computer Systems Science & engineering 2022年第12期43卷 1189-1206页

作者： Mohammad Wedyan Omar Elshaweesh Enas Ramadan Ryan Alturki Department of Autonomous Systems Faculty of Artificial IntelligenceAl-Balqa Applied UniversityAl-Salt19117Jordan Department of Software Engineering Information Technology CollegeAL-Hussein bin Talal UniversityMa’an71111Jordan Department of Computer Science Prince Abdullah Bin Ghazi Faculty of Communication and Information TechnologyAl-Balqa Applied UniversityAl-Salt19117Jordan Department of Information Science College of Computer and Information SystemsUmm Al-Qura UniversityMakkah21961Saudi Arabia

Big data is a term that refers to a set of data that,due to its largeness or complexity,cannot be stored or processed with one of the usual tools or applications for data management,and it has become a prominent word in recent years for the massive development of *** immediately thereafter,the term“big data mining”emerged,i.e.,mining from big data even as an emerging and interconnected field of *** is an important stage in data mining since it helps people make better decisions in a variety of situations,including scientific endeavors,biomedical research,and industrial *** probabilistic neural network(PNN)is a commonly used and successful method for handling classification and pattern recognition *** this study,the authors proposed to combine the probabilistic neural network(PPN),which is one of the data mining techniques,with the vibrating particles system(VPS),which is one of the metaheuristic algorithms named“VPS-PNN”,to solve classi-fication problems more *** data set is eleven common benchmark medical datasets from the machine-learning library,the suggested method was *** suggested VPS-PNN mechanism outperforms the PNN,biogeography-based optimization,enhanced-water cycle algorithm(E-WCA)and the firefly algorithm(FA)in terms of convergence speed and classification accuracy.

关键词： Vibrating particles system(VPS) probabilistic neural network(PNN) classification problem data mining

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Intelligent-Based Extraction of Business Capabilities from Company Descriptions

Intelligent-Based Extraction of Business Capabilities from C...

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International Conference on Information Technology, Information Systems and Electrical engineering (ICITISEE)

作者： Faisal Fahmi Siti Hafizah Ab Hamid Imam Yuadi Department of Library and Information Science Faculty of Political and Social Science Airlangga University Department of Software Engineering Faculty of Computer Science & Information Technology Universiti Malaya

In the era of Big Data, Information can be generated, extracted, and utilized in diverse ways. In business, information about business capabilities can be a crucial aspect in understanding the strengths and competencies of organizations. This Information can empower stakeholders to make informed decisions regarding partnerships, acquisitions, and market positioning. Additionally, by comparing capabilities across companies and industries, organizations can identify their competitive advantages, industry trends, potential disruptions, and effective resource allocations, leading to improved strategies and better market opportunities. Traditional methods for extracting business capabilities primarily rely on keyword matching or rule-based techniques, which often fail to capture the complex relationships between words in a sentence. This paper presents a comprehensive study on extracting business capabilities using dependency parsing techniques in natural language processing (NLP) to overcome limitations of the traditional methods and improve the accuracy of business capability extraction. Dependency parsing utilizes pre-trained language models to analyze the grammatical structure of sentences, identifying the dependencies between words and capturing their relationships. The feasibility and effectiveness of the proposed approach is demonstrated through experiments conducted on more than 40K real-world company descriptions crawled from Wikipedia.

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Modeling of Sensor Enabled IrrigationManagement for Intelligent Agriculture Using Hybrid Deep Belief Network

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computer Systems Science & engineering 2023年第8期46卷 2319-2335页

作者： Saud Yonbawi Sultan Alahmari B.R.S.S.Raju Chukka Hari Govinda Rao Mohamad Khairi Ishak Hend Khalid Alkahtani JoséVarela-Aldás Samih M.Mostafa Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia King Abdul Aziz City for Science and Technology RiyadhKingdom of Saudi Arabia Aditya Engineering College Affiliated to JNTUK(Kakinada) SurampalemIndia Department of MBA Vignan’s Institute of Information and Technology(A)DuvvadaVisakhapatnamAP530049India School of Electrical and Electronic Engineering Engineering CampusUniversiti Sains Malaysia(USM)Nibong TebalPenang14300Malaysia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11564Saudi Arabia SISAu Research Group Universidad Tecnológica IndoaméricaAmbato180103Ecuador Faculty of Computers and Information South Valley UniversityQena83523Egypt

Artificial intelligence(AI)technologies and sensors have recently received significant interest in intellectual *** the application of AI technologies and agriculture sensors in intellectual agriculture is urgently required for the growth of modern agriculture and will help promote smart *** irrigation scheduling systems were highly required in the agricultural field due to their capability to manage and save water deficit irrigation *** learning systems devise an alternative to conventional irrigation management through the automatic elaboration of predictions related to the learning of an *** this motivation,this study develops a modified black widow optimization with a deep belief network-based smart irrigation system(MBWODBN-SIS)for intelligent *** MBWODBN-SIS algorithm primarily enables the Internet of Things(IoT)based sensors to collect data forwarded to the cloud server for examination ***,the MBWODBN-SIS technique applies the deep belief network(DBN)model for different types of irrigation classification:average,high needed,highly not needed,and not *** MBWO algorithm is used for the hyperparameter tuning process.A wideranging experiment was conducted,and the comparison study stated the enhanced outcomes of the MBWODBN-SIS approach to other DL models with maximum accuracy of 95.73%.

关键词： Agriculture smart farming hyperparameter tuning artificial intelligence irrigation management sensors deep learning

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Domain-Specific Scientific Impact and its Prediction

Domain-Specific Scientific Impact and its Prediction

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2021 International Conference on Artificial Intelligence, ICAI 2021

作者： Amjad, Tehmina International Islamic University Faculty of Basic and Applied Sciences Department of Computer Science and Software Engineering Islamabad44000 Pakistan

ISBN: (纸本)9781665432931

In today's interdisciplinary research society where researchers are interested to participate in more than one discipline and they collaborate across disciplines, it is essential to understand the domain-specific impact of scholars. This study explores the impact of scholars who work in multiple disciplines and predicts scientific impact using multiple features. A Miner dataset is selected as a testbed and 10 sub-domains of computer science are analyzed. Despite today's cross-discipline collaboration trend, there is a high number of scholars who work in a single domain as compared to those who work in multiple domains. Results show that among the 10 disciplines of computer science, 'Network Control' gained high impact in terms of received citations. Various features are used to predict the future impact and 'total papers' yield the highest R2 value followed by 'topic-based citations' and 'total citations'. © 2021 IEEE.

关键词： Forecasting

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Impact of Extreme Class-Imbalance on Landslide-Risk Prediction and Mitigation Using Two-Stage Deep Neural Network 14

Impact of Extreme Class-Imbalance on Landslide-Risk Predicti...

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14th International Conference on Signal Processing Systems, ICSPS 2022

作者： Tengtrairat, N. Woo, W.L. Parathai, P. Sundaranaga, C. Na Ayutthaya, T. Kridakorn Rinchumphu, D. School of Software Engineering Payap University Chiang Mai Thailand Northumbria University Department of Computer and Information Sciences Newcastle upon Tyne United Kingdom Chiang Mai University City Research and Development Center Faculty of Engineering Chiang Mai Thailand Chiang Mai University Faculty of Engineering Department of Civil Engineering Chiang Mai Thailand

ISBN: (纸本)9798350336313

The classification of landslides is the one of the most challenging topics because of the complexity of the relationships of various dynamic and uncertain factors and the physical gaining data processes. Landslide incidents frequently occur in the upper northern region of Thailand due to its topography. The landslide classification method is proposed to capture the significant features from an extreme case of class-imbalance dataset. The imbalanced nature of some real-world data is one of the current challenges for machine learning researchers. A study area of this research covers an area of 25 square kilometers at Chiang Rai, in Thailand which contains 30,408 non-landslide data and 1,077 landslide data. The percentage of landslide data is 3.54% of the total data. This paper proposed the solution of landslide classification given by extreme class-imbalance dataset. The proposed method has two main steps i.e., firstly, mitigating class-imbalance dataset. and secondly two-stage learning. Performance of the proposed method benchmarks the baseline, the logistic regression (LR), the random forest classifier (RFC) methods given by enhanced dataset. In the case of imbalance dataset, the one-class method is assessed against the proposed method along with the LR and the RFC methods. Experimental results demonstrate that the proposed method has improved the landslide-risk prediction performance over the baseline, the LR, the RFC and the one-class SVM methods in terms of an average area under the curve scores by 0.48, 0.48, 0.03, and 0.06, respectively, in both enhanced dataset and imbalance dataset. © 2022 IEEE.

关键词： Landslides

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Detection of Toxic Content on Social Networking Platforms Using Fine Tuned ULMFiT Model

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Intelligent Automation & Soft Computing 2023年第1期35卷 15-30页

作者： Hafsa Naveed Abid Sohail Jasni Mohamad Zain Noman Saleem Rao Faizan Ali Shahid Anwar Department of Software Engineering Faculty of ScienceUniversity of LahorePakistan Department of Computer Science COMSATS University IslamabadLahore CampusPakistan Institute for Big Data Analyics and Artifcial Intelligence(IBDAAI) Kompleks Al-KhawarizmiUniversii Tekoogi MARA40450Shah AlamSelangorMalaysia TechnoGenics SMC PVT LTD LahorePakistan Department of Computer and Infomation Science Universiti Teknologi PETRONASBandar Ser IskandarTronohPerakMalaysia Depatment of Information Engineering Technology National Skils University IslamabadSector H-8/1Faiz AhmedFaiz RoadIslamabadPakistan

Question and answer websites such as Quora,Stack Overflow,Yahoo Answers and Answer Bag are used by *** users post questions on these websites to get the answers from domain specific *** websites are multilingual meaning they are available in many different *** problem for these types of websites is to handle meaningless and irrelevant *** this paper we have worked on the Quora insincere questions(questions which are based on false assumptions or questions which are trying to make a statement rather than seeking for helpful answers)dataset in order to identify user insincere questions,so that Quora can eliminate those questions from their platform and ultimately improve the communication among users over the ***,a research was carried out with recurrent neural network and pretrained glove word embeddings,that achieved the F1 score of *** proposed study has used a pre-trained ULMFiT *** model has outperformed the previous model with an F1 score of 0.91,which is much higher than the previous studies.

关键词： Machine learning text mining quora mining artificial intelligence natural language processing

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