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An Efficient and Secure Energy Trading Approach with Machine Learning Technique and Consortium Blockchain.
Ashfaq, Tehreem; Khalid, Muhammad Irfan; Ali, Gauhar; Affendi, Mohammad El; Iqbal, Jawaid; Hussain, Saddam; Ullah, Syed Sajid; Yahaya, Adamu Sani; Khalid, Rabiya; Mateen, Abdul.
Affiliation
  • Ashfaq T; Department of Computer Science, COMSATS University Islamabad, Islamabad 44000, Pakistan.
  • Khalid MI; Department of Information and Electrical Engineering and Applied Mathematics, University of Salerno, 84084 Fisciano, SA, Italy.
  • Ali G; EIAS Data Science and Blockchain Lab, College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia.
  • Affendi ME; EIAS Data Science and Blockchain Lab, College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia.
  • Iqbal J; Department of Computer Science, Capital University of Science and Technology, Islamabad 44000, Pakistan.
  • Hussain S; School of Digital Science, Universiti Brunei Darussalam, Jalan Tungku Link, Gadong BE1410, Brunei.
  • Ullah SS; Department of Electrical and Computer Engineering, Villanova University, Villanova, PA 19085, USA.
  • Yahaya AS; Department of Information and Communication Technology, University of Agder (UiA), N-4898 Grimstad, Norway.
  • Khalid R; Department of Computer Science, COMSATS University Islamabad, Islamabad 44000, Pakistan.
  • Mateen A; Department of Computer Science, COMSATS University Islamabad, Islamabad 44000, Pakistan.
Sensors (Basel) ; 22(19)2022 Sep 25.
Article in En | MEDLINE | ID: mdl-36236363
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Blockchain Type of study: Prognostic_studies Language: En Journal: Sensors (Basel) Year: 2022 Document type: Article Affiliation country: Pakistán
Fulltext
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Blockchain Type of study: Prognostic_studies Language: En Journal: Sensors (Basel) Year: 2022 Document type: Article Affiliation country: Pakistán