{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,21]],"date-time":"2026-06-21T04:28:07Z","timestamp":1782016087754,"version":"3.54.5"},"reference-count":58,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2022,6,19]],"date-time":"2022-06-19T00:00:00Z","timestamp":1655596800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Cloud Computing (CC) provides a combination of technologies that allows the user to use the most resources in the least amount of time and with the least amount of money. CC semantics play a critical role in ranking heterogeneous data by using the properties of different cloud services and then achieving the optimal cloud service. Regardless of the efforts made to enable simple access to this CC innovation, in the presence of various organizations delivering comparative services at varying cost and execution levels, it is far more difficult to identify the ideal cloud service based on the user\u2019s requirements. In this research, we propose a Cloud-Services-Ranking Agent (CSRA) for analyzing cloud services using end-users\u2019 feedback, including Platform as a Service (PaaS), Infrastructure as a Service (IaaS), and Software as a Service (SaaS), based on ontology mapping and selecting the optimal service. The proposed CSRA possesses Machine-Learning (ML) techniques for ranking cloud services using parameters such as availability, security, reliability, and cost. Here, the Quality of Web Service (QWS) dataset is used, which has seven major cloud services categories, ranked from 0\u20136, to extract the required persuasive features through Sequential Minimal Optimization Regression (SMOreg). The classification outcomes through SMOreg are capable and demonstrate a general accuracy of around 98.71% in identifying optimum cloud services through the identified parameters. The main advantage of SMOreg is that the amount of memory required for SMO is linear. The findings show that our improved model in terms of precision outperforms prevailing techniques such as Multilayer Perceptron (MLP) and Linear Regression (LR).<\/jats:p>","DOI":"10.3390\/s22124627","type":"journal-article","created":{"date-parts":[[2022,6,19]],"date-time":"2022-06-19T21:19:26Z","timestamp":1655673566000},"page":"4627","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["InteliRank: A Four-Pronged Agent for the Intelligent Ranking of Cloud Services Based on End-Users\u2019 Feedback"],"prefix":"10.3390","volume":"22","author":[{"given":"Muhammad Munir","family":"Ud Din","sequence":"first","affiliation":[{"name":"School of Computer Sciences, National College of Business Administration & Economics, Lahore 54700, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nasser","family":"Alshammari","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Computer and Information Sciences, Jouf University, Sakaka 72341, Aljouf, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1714-1948","authenticated-orcid":false,"given":"Saad Awadh","family":"Alanazi","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Computer and Information Sciences, Jouf University, Sakaka 72341, Aljouf, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5269-0427","authenticated-orcid":false,"given":"Fahad","family":"Ahmad","sequence":"additional","affiliation":[{"name":"Department of Basic Sciences, Jouf University, Sakaka 72341, Aljouf, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0791-541X","authenticated-orcid":false,"given":"Shahid","family":"Naseem","sequence":"additional","affiliation":[{"name":"Department of Information Sciences, Division of Sciences and Technology, University of Education, Lahore 54770, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad Saleem","family":"Khan","sequence":"additional","affiliation":[{"name":"School of Computer Sciences, National College of Business Administration & Economics, Lahore 54700, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hafiz Syed Imran","family":"Haider","sequence":"additional","affiliation":[{"name":"Department of Software Engineering, University of Lahore, Lahore 54770, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1145\/3460123","article-title":"Design and Realisation of Scalable Business Process Management Systems for Deployment in the Cloud","volume":"12","author":"Ouyang","year":"2021","journal-title":"ACM Trans. Manag. Inf. Syst."},{"key":"ref_2","first-page":"288","article-title":"Securing cognitive radio vehicular ad hoc network with fog node based distributed blockchain cloud architecture","volume":"10","author":"Nadeem","year":"2019","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_3","first-page":"1271","article-title":"Intrusion detection and prevention in cloud computing using genetic algorithm","volume":"5","author":"Hameed","year":"2014","journal-title":"Int. J. Sci. Eng. Res."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"15541","DOI":"10.1007\/s11042-021-10616-6","article-title":"Energy, network, and application-aware virtual machine placement model in SDN-enabled large scale cloud data centers","volume":"80","author":"Rawas","year":"2021","journal-title":"Multimed. Tools Appl."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"114390","DOI":"10.1016\/j.eswa.2020.114390","article-title":"Trustworthiness prediction of cloud services based on selective neural network ensemble learning","volume":"168","author":"Mao","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Hassan, T., and Ahmed, F. (2018, January 23\u201325). Transaction and Identity Authentication Security Model for E-Banking: Confluence of Quantum Cryptography and AI. Proceedings of the International Conference on Intelligent Technologies and Applications, Bahawalpur, Pakistan.","DOI":"10.1007\/978-981-13-6052-7_29"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"6138434","DOI":"10.1155\/2022\/6138434","article-title":"Edge Caching in Fog-Based Sensor Networks through Deep Learning-Associated Quantum Computing Framework","volume":"2022","author":"Hasan","year":"2022","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Aslam, B., Abid, R., Rizwan, M., Ahmad, F., and Sattar, M.U. (2019, January 24\u201325). Heterogeneity Model for Wireless Mobile Cloud Computing & its Future Challenges. Proceedings of the 2019 International Conference on Electrical, Communication, and Computer Engineering (ICECCE), Swat, Pakistan.","DOI":"10.1109\/ICECCE47252.2019.8940681"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"101840","DOI":"10.1016\/j.is.2021.101840","article-title":"The convergence and interplay of edge, fog, and cloud in the AI-driven Internet of Things (IoT)","volume":"107","author":"Firouzi","year":"2021","journal-title":"Inf. Syst. J."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Rizwan, M., Shabbir, A., Shabbir, M., Ahmad, F., and Sattar, M.U. (2019, January 1\u20132). A Clustering based Hybrid Mobility in WPAN. Proceedings of the 2019 International Conference on Innovative Computing (ICIC), Lahore, Pakistan.","DOI":"10.1109\/ICIC48496.2019.8966743"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Soh, J., Copeland, M., Puca, A., and Harris, M. (2020). Overview of Azure Infrastructure as a Service (IaaS) Services. Microsoft Azure, Springer.","DOI":"10.1007\/978-1-4842-5958-0"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"8379532","DOI":"10.1155\/2022\/8379532","article-title":"Prevention Techniques against Distributed Denial of Service Attacks in Heterogeneous Networks: A Systematic Review","volume":"2022","author":"Cheema","year":"2022","journal-title":"Secur. Commun. Netw."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Silverstein, J.C., and Foster, I.T. (2014). Computer architectures for health care and biomedicine. Biomedical Informatics, Springer.","DOI":"10.1007\/978-1-4471-4474-8_5"},{"key":"ref_14","first-page":"305","article-title":"Security of Computer Networks Implemented in Universities and Business Environment","volume":"7","author":"IONESCU","year":"2014","journal-title":"Hyperion Int. J. Econophys. New Econ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1007\/s10723-021-09570-2","article-title":"Serverless Workflows for Containerised Applications in the Cloud Continuum","volume":"19","author":"Risco","year":"2021","journal-title":"J. Grid Comput."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Butpheng, C., Yeh, K.-H., and Xiong, H. (2020). Security and privacy in IoT-cloud-based e-health systems\u2014A comprehensive review. Symmetry, 12.","DOI":"10.3390\/sym12071191"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"102050","DOI":"10.1016\/j.rcim.2020.102050","article-title":"Cloud manufacturing ecosystem analysis and design","volume":"67","author":"Helo","year":"2021","journal-title":"Robot. Comput.-Integr. Manuf."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Saraswat, M., and Tripathi, R. (2020, January 4\u20135). Cloud Computing: Analysis of Top 5 CSPs in SaaS, PaaS and IaaS Platforms. Proceedings of the 2020 9th International Conference System Modeling and Advancement in Research Trends (SMART), Moradabad, India.","DOI":"10.1109\/SMART50582.2020.9337157"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Hassan, T., Khan, W.A., Ahmad, F., Rizwan, M., and Rehman, R. (2019, January 6\u20138). Edge Caching Framework in Fog Based Radio Access Networks Through AI in Quantum Regime. Proceedings of the International Conference on Intelligent Technologies and Applications, Bahawalpur, Pakistan.","DOI":"10.1007\/978-981-15-5232-8_61"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Bokhari, M.U., Makki, Q., and Tamandani, Y.K. (2018). A survey on cloud computing. Big Data Analytics, Springer.","DOI":"10.1007\/978-981-10-6620-7_16"},{"key":"ref_21","first-page":"1468","article-title":"Cognitively managed multi-level authentication for security using Fuzzy Logic based Quantum Key Distribution","volume":"34","author":"Shabbir","year":"2022","journal-title":"J. King. Saud. Univ.\u2014Comput. Inf. Sci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/j.eij.2022.01.003","article-title":"Trust identification through cognitive correlates with emphasizing attention in cloud robotics","volume":"23","author":"Khan","year":"2022","journal-title":"Egypt. Inform. J."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"35946","DOI":"10.1109\/ACCESS.2018.2847631","article-title":"P-CSREC: A new approach for personalized cloud service recommendation","volume":"6","author":"Zhang","year":"2018","journal-title":"IEEE Access"},{"key":"ref_24","first-page":"1","article-title":"Cloud Computing Serving as a Solution to the IoT Generated Data","volume":"11","author":"Rizwan","year":"2018","journal-title":"Bahria Univ. J. Inf. Commun. Technol."},{"key":"ref_25","unstructured":"Carney, W.T. (2019). A Case Study of the United States Air Force Adoption of Cloud Computing. [Ph.D. Thesis, Robert Morris University]."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Alanazi, S.A., Alruwaili, M., Ahmad, F., Alaerjan, A., and Alshammari, N. (2021). Estimation of Organizational Competitiveness by a Hybrid of One-Dimensional Convolutional Neural Networks and Self-Organizing Maps Using Physiological Signals for Emotional Analysis of Employees. Sensors, 21.","DOI":"10.3390\/s21113760"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"7816200","DOI":"10.1155\/2022\/7816200","article-title":"Systematic Framework to Predict Early-Stage Liver Carcinoma Using Hybrid of Feature Selection Techniques and Regression Techniques","volume":"2022","author":"Mehmood","year":"2022","journal-title":"Complexity"},{"key":"ref_28","first-page":"2723","article-title":"Machine Learning Empowered Security Management and Quality of Service Provision in SDN-NFV Environment","volume":"66","author":"Shahzadi","year":"2021","journal-title":"Comput. Mater. Contin."},{"key":"ref_29","first-page":"2265","article-title":"Prediction of COVID-19 cases using machine learning for effective public health management","volume":"66","author":"Ahmad","year":"2021","journal-title":"Comput. Mater. Contin."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Tsiakmaki, M., Kostopoulos, G., Kotsiantis, S., and Ragos, O. (2020). Implementing AutoML in educational data mining for prediction tasks. Appl. Sci., 10.","DOI":"10.3390\/app10010090"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1145\/3280989","article-title":"Parallel computing of support vector machines: A survey","volume":"51","author":"Tavara","year":"2019","journal-title":"ACM Comput. Surv."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1871","DOI":"10.1177\/1475921719898862","article-title":"Design of a new low-cost unmanned aerial vehicle and vision-based concrete crack inspection method","volume":"19","author":"Lei","year":"2020","journal-title":"Struct. Health Monit."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1016\/j.compbiomed.2017.06.006","article-title":"Classification of ECG heartbeats using nonlinear decomposition methods and support vector machine","volume":"87","author":"Rajesh","year":"2017","journal-title":"Comput. Biol. Med."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Kayes, A., Kalaria, R., Sarker, I.H., Islam, M., Watters, P.A., Ng, A., Hammoudeh, M., Badsha, S., and Kumara, I. (2020). A survey of context-aware access control mechanisms for cloud and fog networks: Taxonomy and open research issues. Sensors, 20.","DOI":"10.3390\/s20092464"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"556","DOI":"10.1016\/j.comcom.2020.01.004","article-title":"Energy aware edge computing: A survey","volume":"151","author":"Jiang","year":"2020","journal-title":"Comput. Commun."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.jpdc.2018.12.005","article-title":"Towards evaluation of cloud ontologies","volume":"126","author":"Hassan","year":"2019","journal-title":"J. Parallel Distrib. Comput."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"837","DOI":"10.1007\/s10586-019-02954-w","article-title":"Optimizing virtual machine placement in IaaS data centers: Taxonomy, review and open issues","volume":"23","author":"Talebian","year":"2020","journal-title":"Clust. Comput."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"102217","DOI":"10.1016\/j.rcim.2021.102217","article-title":"Service-oriented industrial internet of things gateway for cloud manufacturing","volume":"73","author":"Liu","year":"2022","journal-title":"Robot. Comput. -Integr. Manuf."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.knosys.2018.02.024","article-title":"HAR-SI: A novel hybrid article recommendation approach integrating with social information in scientific social network","volume":"148","author":"Wang","year":"2018","journal-title":"Knowl. Based Syst."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Zhang, W.E., and Sheng, Q.Z. (2018). Managing Data from Knowledge Bases: Querying and Extraction, Springer.","DOI":"10.1007\/978-3-319-94935-2"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Anisha, C., and Saranya, K. (2022). An Intense Study on Intelligent Service Provisioning for Multi-Cloud Based on Machine Learning Techniques. Operationalizing Multi-Cloud Environments, Springer.","DOI":"10.1007\/978-3-030-74402-1_10"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1016\/j.eswa.2018.09.029","article-title":"Machine learning based phishing detection from URLs","volume":"117","author":"Sahingoz","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Mohammed, F., Ali, A.M., Al-Ghamdi, A.S.A.-M., Alsolami, F., Shamsuddin, S.M., and Eassa, F.E. (2020). Cloud computing services: Taxonomy of discovery approaches and extraction solutions. Symmetry, 12.","DOI":"10.3390\/sym12081354"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2509898","DOI":"10.1155\/2019\/2509898","article-title":"Ensuring the Confidentiality of Nuclear Information at Cloud Using Modular Encryption Standard","volume":"2019","author":"Shabbir","year":"2019","journal-title":"Secur. Commun. Netw."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1639","DOI":"10.1007\/s10922-020-09553-w","article-title":"Service selection using multi-criteria decision making: A comprehensive overview","volume":"28","author":"Hosseinzadeh","year":"2020","journal-title":"J. Netw. Syst. Manag."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"709","DOI":"10.1007\/s12652-018-0721-7","article-title":"SEFAP: An efficient approach for ranking skyline web services","volume":"10","author":"Ouadah","year":"2019","journal-title":"J. Ambient. Intell. Humaniz. Comput."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"597","DOI":"10.1142\/S0219622021500152","article-title":"Integrated Ranking Algorithm for Efficient Decision Making","volume":"20","author":"Deepa","year":"2021","journal-title":"Int. J. Inf. Technol. Decis. Mak."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"2617","DOI":"10.1007\/s11276-016-1301-4","article-title":"Network selection in heterogeneous wireless networks using multi-criteria decision-making algorithms: A review","volume":"23","author":"Obayiuwana","year":"2017","journal-title":"Wirel. Netw."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.future.2019.09.053","article-title":"IIVIFS-WASPAS: An integrated Multi-Criteria Decision-Making perspective for cloud service provider selection","volume":"103","author":"Gireesha","year":"2020","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Mehmood, M., Alshammari, N., Alanazi, S.A., Basharat, A., Ahmad, F., Sajjad, M., and Junaid, K. (J. King. Saud. Univ.-Comput. Inf. Sci., 2022). Improved Colorization and Classification of Intracranial Tumor Expanse in MRI Images via Hybrid Scheme of Pix2Pix-cGANs and NASNet-Large, J. King. Saud. Univ.-Comput. Inf. Sci., in press.","DOI":"10.1016\/j.jksuci.2022.05.015"},{"key":"ref_51","first-page":"107","article-title":"A survey on trust evaluation based on machine learning","volume":"53","author":"Wang","year":"2020","journal-title":"ACM Comput. Surv."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"41","DOI":"10.3390\/bdcc5030041","article-title":"A review of artificial intelligence, Big Data, and blockchain technology applications in medicine and global health","volume":"5","author":"Chattu","year":"2021","journal-title":"Big Data Cogn. Comput."},{"key":"ref_53","first-page":"641","article-title":"Machine learning enabled early detection of breast cancer by structural analysis of mammograms","volume":"67","author":"Mehmood","year":"2021","journal-title":"Comput. Mater. Contin."},{"key":"ref_54","first-page":"e4","article-title":"ANFIS based hybrid approach identifying correlation between decision making and online social networks","volume":"8","author":"Rahman","year":"2021","journal-title":"EAI Endorsed Trans. Scal. Inf. Syst."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"63173","DOI":"10.1109\/ACCESS.2021.3074374","article-title":"Fuzzy Logic Based Prospects Identification System for Foreign Language Learning Through Serious Games","volume":"9","author":"Yanes","year":"2021","journal-title":"IEEE Access"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"5352108","DOI":"10.1155\/2020\/5352108","article-title":"Security of cloud computing using adaptive neural Fuzzy inference system","volume":"2020","author":"Shahzadi","year":"2020","journal-title":"Secur. Commun. Netw."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"4561","DOI":"10.1007\/s11269-020-02672-8","article-title":"Application of artificial neural networks, support vector machine and multiple model-ANN to sediment yield prediction","volume":"34","author":"Meshram","year":"2020","journal-title":"Water Resour. Manag."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"624","DOI":"10.1080\/02626667.2019.1703186","article-title":"River suspended sediment load prediction based on river discharge information: Application of newly developed data mining models","volume":"65","author":"Salih","year":"2020","journal-title":"Hydrol. Sci. J."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/12\/4627\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:35:10Z","timestamp":1760139310000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/12\/4627"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,19]]},"references-count":58,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2022,6]]}},"alternative-id":["s22124627"],"URL":"https:\/\/doi.org\/10.3390\/s22124627","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,19]]}}}