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This Author published in this journals
All Journal Computer Science and Information Technologies Journal of Computing Theories and Applications
Morolake Oladayo Lawrence
Baze University
Computer Science & IT Decision Sciences, Operations Research & Management Engineering

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RETRACTED: The Use of AI to Analyze Social Media Attacks for Predictive Analytics Temitope Samson Adekunle; Oluwaseyi Omotayo Alabi; Morolake Oladayo Lawrence; Godwin Nse Ebong; Grace Oluwamayowa Ajiboye; Temitope Abiodun Bamisaye
Journal of Computing Theories and Applications Vol. 1 No. 4 (2024): JCTA 1(4) 2024
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.10120

Abstract

This article has been retracted at the request of the Editor-in-Chief. The journal was alerted to issues within this article, including significant overlap in content, methodology, and visual materials with another previously published article: "Social Engineering Attack Classifications on Social Media Using Deep Learning" (DOI: 10.32604/cmc.2023.032373) published in Computers, Materials & Continua in 2023. Upon thorough investigation, it was found that the article substantially reproduces ideas, methodologies, and figures from the original work without proper attribution, violating the ethical standards of the journal and academic publishing. The authors were contacted and asked to provide an explanation for these concerns. The corresponding author acknowledged the oversight and accepted responsibility for the duplication. Consequently, the authors formally requested the withdrawal of the paper. As per journal policy, the Editor-in-Chief has decided to retract the article due to a breach of publication ethics. The journal sincerely regrets that these issues were not detected during the manuscript screening and review process and apologizes to the authors of the original article, as well as to the readers of the journal. For more information on the journal’s ethical policies, please visit: Retraction Policy.
Deep learning technique for plant disease detection Temitope Samson Adekunle; Morolake Oladayo Lawrence; Oluwaseyi Omotayo Alabi; Adenrele A. Afolorunso; Godwin Nse Ebong; Matthew Abiola Oladipupo
Computer Science and Information Technologies Vol 5, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v5i1.p55-62

Abstract

A nation's economy is primarily reliant on agricultural growth. However, several plant diseases seriously impair crop growth, both in terms of quantity and quality. Due to a lack of subject matter specialists and low contrast data, accurate diagnosis of many diseases by hand is highly difficult and time-consuming. The farm management system is therefore looking for a method for automatically detecting early illnesses. To overcome these challenges and correctly classify the different diseases, an efficient and small deep learning-based framework (E-GreenNet) is proposed. A MobileNetV3Small model is used as the foundation of our end-to-end architecture to produce finely tuned, discriminative, and noticeable features. Furthermore, the new plant composite (PC), plantvillage (PV), and data repository of leaf images (DRLI) datasets are used to independently train our proposed model, and test samples are used to evaluate its actual performance. The suggested model achieved accuracy rates of 1.00 percent, 0.96 percent, and 0.99 percent on the given datasets after a rigorous experimental study. Additionally, a comparative investigation of our proposed technique against the state-of-the-art (SOTA) reveals extremely high discriminative scores.
Co-Authors Adenrele A. Afolorunso Godwin Nse Ebong Godwin Nse Ebong Grace Oluwamayowa Ajiboye Matthew Abiola Oladipupo Oluwaseyi Omotayo Alabi Temitope Abiodun Bamisaye Temitope Samson Adekunle